diff --git a/.gitignore b/.gitignore
index 9a2600b20..74bea66ff 100644
--- a/.gitignore
+++ b/.gitignore
@@ -25,4 +25,6 @@ test_notebooks/
 voc_representations.h5
 VOCdevkit/
 VOCtrainval_06-Nov-2007.tar.gz
-node_modules/
\ No newline at end of file
+node_modules/.venv/
+Azure_setup.md
+uv.lock
diff --git a/01_materials/labs/lab_1.ipynb b/01_materials/labs/lab_1.ipynb
index 25b751340..9224e053c 100644
--- a/01_materials/labs/lab_1.ipynb
+++ b/01_materials/labs/lab_1.ipynb
@@ -31,9 +31,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "GPUs disabled, using CPU.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2026-02-16 22:12:32.449359: E external/local_xla/xla/stream_executor/cuda/cuda_platform.cc:51] failed call to cuInit: INTERNAL: CUDA error: Failed call to cuInit: CUDA_ERROR_NO_DEVICE: no CUDA-capable device is detected\n"
+     ]
+    }
+   ],
    "source": [
     "## Tensorflow GPU disabled by default. Comment the following lines to enable GPU if you have a working CUDA installation.\n",
     "import tensorflow as tf\n",
@@ -54,7 +69,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -70,9 +85,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 4,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(1797, 8, 8)"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "digits.images.shape"
    ]
@@ -88,9 +114,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 5,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(1797, 64)"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "digits.data.shape"
    ]
@@ -106,9 +143,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 6,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(1797,)"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "digits.target.shape"
    ]
@@ -124,9 +172,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 7,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 600x600 with 9 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "# Selecting 9 random indices\n",
     "random_indices = np.random.choice(len(digits.images), 9, replace=False)\n",
@@ -156,11 +215,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 8,
    "metadata": {
     "collapsed": false
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 600x600 with 9 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "# Selecting 9 random indices of images labelled as 9\n",
     "random_indices = np.random.choice(np.where(digits.target == 9)[0], 9, replace=False)\n",
@@ -201,7 +271,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -227,11 +297,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 10,
    "metadata": {
     "collapsed": false
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "X_train shape: (1437, 64)\n",
+      "y_train shape: (1437,)\n",
+      "X_test shape: (360, 64)\n",
+      "y_test shape: (360,)\n"
+     ]
+    }
+   ],
    "source": [
     "print(f'X_train shape: {X_train.shape}')\n",
     "print(f'y_train shape: {y_train.shape}')\n",
@@ -265,9 +346,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 11,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Before one-hot encoding: 6\n",
+      "After one-hot encoding: [0. 0. 0. 0. 0. 0. 1. 0. 0. 0.]\n"
+     ]
+    }
+   ],
    "source": [
     "from tensorflow.keras.utils import to_categorical\n",
     "\n",
@@ -298,11 +388,93 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 12,
    "metadata": {
     "collapsed": false
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"sequential\"</span>\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[1mModel: \"sequential\"\u001b[0m\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓\n",
+       "┃<span style=\"font-weight: bold\"> Layer (type)                         </span>┃<span style=\"font-weight: bold\"> Output Shape                </span>┃<span style=\"font-weight: bold\">         Param # </span>┃\n",
+       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩\n",
+       "│ dense (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                        │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)                  │           <span style=\"color: #00af00; text-decoration-color: #00af00\">4,160</span> │\n",
+       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
+       "│ dense_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)                  │           <span style=\"color: #00af00; text-decoration-color: #00af00\">4,160</span> │\n",
+       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
+       "│ dense_2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">10</span>)                  │             <span style=\"color: #00af00; text-decoration-color: #00af00\">650</span> │\n",
+       "└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓\n",
+       "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                        \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape               \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m        Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
+       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩\n",
+       "│ dense (\u001b[38;5;33mDense\u001b[0m)                        │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m)                  │           \u001b[38;5;34m4,160\u001b[0m │\n",
+       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
+       "│ dense_1 (\u001b[38;5;33mDense\u001b[0m)                      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m)                  │           \u001b[38;5;34m4,160\u001b[0m │\n",
+       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
+       "│ dense_2 (\u001b[38;5;33mDense\u001b[0m)                      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m10\u001b[0m)                  │             \u001b[38;5;34m650\u001b[0m │\n",
+       "└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">8,970</span> (35.04 KB)\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m8,970\u001b[0m (35.04 KB)\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">8,970</span> (35.04 KB)\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m8,970\u001b[0m (35.04 KB)\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "from tensorflow.keras.models import Sequential\n",
     "from tensorflow.keras.layers import Input, Dense\n",
@@ -335,7 +507,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 13,
    "metadata": {
     "collapsed": false
    },
@@ -365,11 +537,38 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 14,
    "metadata": {
     "collapsed": false
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 1/5\n",
+      "\u001b[1m36/36\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - accuracy: 0.5979 - loss: 1.4495 - val_accuracy: 0.8056 - val_loss: 0.6434\n",
+      "Epoch 2/5\n",
+      "\u001b[1m36/36\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8634 - loss: 0.4532 - val_accuracy: 0.8993 - val_loss: 0.3752\n",
+      "Epoch 3/5\n",
+      "\u001b[1m36/36\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9112 - loss: 0.2989 - val_accuracy: 0.8993 - val_loss: 0.3350\n",
+      "Epoch 4/5\n",
+      "\u001b[1m36/36\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9426 - loss: 0.2162 - val_accuracy: 0.9271 - val_loss: 0.2656\n",
+      "Epoch 5/5\n",
+      "\u001b[1m36/36\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9591 - loss: 0.1821 - val_accuracy: 0.9306 - val_loss: 0.2370\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<keras.src.callbacks.history.History at 0x7901e7def250>"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "model.fit(\n",
     "    X_train, # Training data\n",
@@ -393,11 +592,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 15,
    "metadata": {
     "collapsed": false
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9361 - loss: 0.2082  \n",
+      "Loss:     0.21\n",
+      "Accuracy: 93.61%\n"
+     ]
+    }
+   ],
    "source": [
     "loss, accuracy = model.evaluate(X_test, y_test)\n",
     "\n",
@@ -416,11 +625,29 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 16,
    "metadata": {
     "collapsed": false
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step \n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 600x600 with 9 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "# Get the predictions for the test data\n",
     "predictions = model.predict(X_test)\n",
@@ -493,7 +720,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -503,7 +730,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -512,7 +739,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 19,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -534,7 +761,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 20,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -544,7 +771,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 21,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -564,9 +791,35 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 22,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<tf.Tensor: shape=(5, 10), dtype=float32, numpy=\n",
+       "array([[6.5549411e-04, 4.6668509e-05, 2.9153343e-05, 9.7057688e-05,\n",
+       "        6.6982961e-04, 7.4186275e-05, 9.9815893e-01, 2.9780356e-05,\n",
+       "        1.3402854e-04, 1.0475160e-04],\n",
+       "       [6.3361181e-04, 2.2407472e-05, 5.7929868e-05, 5.5981806e-04,\n",
+       "        1.2253279e-04, 7.4779175e-02, 2.9937237e-06, 3.4049321e-03,\n",
+       "        8.8742524e-03, 9.1154218e-01],\n",
+       "       [5.5525146e-05, 2.6222619e-05, 2.5637388e-05, 9.9888098e-01,\n",
+       "        3.3590371e-08, 5.1596340e-05, 2.4855942e-06, 9.2382441e-05,\n",
+       "        4.5514142e-04, 4.0978219e-04],\n",
+       "       [1.0585393e-02, 5.5865217e-03, 1.1532486e-02, 3.4974548e-03,\n",
+       "        2.4629151e-03, 1.7727071e-02, 5.3529482e-04, 8.8688797e-01,\n",
+       "        3.1427063e-02, 2.9757740e-02],\n",
+       "       [8.4058382e-05, 4.7221689e-05, 9.9096102e-01, 1.8281703e-03,\n",
+       "        2.6253466e-07, 1.4611852e-03, 3.6532159e-03, 9.6096061e-05,\n",
+       "        1.8452933e-03, 2.3451301e-05]], dtype=float32)>"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "predictions_tf = model(X_test)\n",
     "predictions_tf[:5]"
@@ -574,9 +827,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 23,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(tensorflow.python.framework.ops.EagerTensor, TensorShape([360, 10]))"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "type(predictions_tf), predictions_tf.shape"
    ]
@@ -590,9 +854,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 24,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<tf.Tensor: shape=(5,), dtype=float32, numpy=\n",
+       "array([0.9999999 , 0.9999998 , 0.9999998 , 0.99999994, 1.        ],\n",
+       "      dtype=float32)>"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "import tensorflow as tf\n",
     "\n",
@@ -617,9 +894,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 25,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<tf.Tensor: shape=(5,), dtype=int64, numpy=array([6, 9, 3, 7, 2])>"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "predicted_labels_tf = tf.argmax(predictions_tf, axis=1)\n",
     "predicted_labels_tf[:5]"
@@ -636,11 +924,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 26,
    "metadata": {
     "collapsed": false
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAeQAAAH4CAYAAACbup4ZAAAAOnRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjEwLjgsIGh0dHBzOi8vbWF0cGxvdGxpYi5vcmcvwVt1zgAAAAlwSFlzAAAPYQAAD2EBqD+naQAAG+ZJREFUeJzt3W9onfX5+PErXeZQm7R06iA0bZyobK22xcGw22h8MhFXzQNXpxOMnbCKc2b4YDh06zZ0YwwaRVgnm1awhX3tMIob2ygs/q1DlDr2jxZHSpug+C9N/FNtm/N78KVlsq/+Tvq5k/tKz+sFPjltr3N5+sn97knT3G2NRqMRAECt5tW9AAAgyACQgiADQAKCDAAJCDIAJCDIAJCAIANAAoIMAAkIMgAkIMgAkIAgN+m5556Lb33rW7Fs2bI49dRTY8mSJbFu3brYvXt33atBRETccccd0dbWFsuXL697FVpMf39/tLW1feh/o6Ojda84J7T5XtbNueKKK+Lpp5+Or371q3H++efHyy+/HPfcc0+89dZb8eyzz7oIUqv9+/fHueeeG21tbdHT0xN/+9vf6l6JFrJz58546aWXPvBYo9GIDRs2RE9PT/z973+vabO5RZCb9Mwzz8TnPve5OOmkk449tmfPnjjvvPPiiiuuiAcffLDG7Wh1X/va1+LVV1+NI0eOxGuvvSbI1O6pp56KL33pS3HHHXfE9773vbrXmRN8yrpJq1ev/kCMIyLOPvvsWLZsWfzzn/+saSuIeOKJJ2L79u0xODhY9ypwzLZt26KtrS2uvvrquleZMwS5QKPRiFdeeSVOO+20ulehRR05ciRuuummuP766+O8886rex2IiIhDhw7F//zP/8Tq1aujp6en7nXmjPa6F5jLtm7dGqOjo/GjH/2o7lVoUZs3b469e/fGjh076l4FjvnjH/8Yr7/+enz961+ve5U5xTvk4/Svf/0rbrzxxrjwwgvj2muvrXsdWtDrr78e3//+9+P222+P008/ve514Jht27bFxz/+8Vi3bl3dq8wpgnwcXn755bj00ktjwYIFsX379vjYxz5W90q0oNtuuy0WLVoUN910U92rwDFvvfVWPPLII3HxxRfHJz/5ybrXmVN8ynqaDhw4EJdcckmMj4/Hk08+GV1dXXWvRAvas2dP3HvvvTE4OBhjY2PHHj948GAcOnQoRkZGorOzMxYtWlTjlrSioaGheOedd3y6+jj4Z0/TcPDgwfjyl78czz//fOzYsSMuvPDCuleiRQ0PD8dFF130kT/n5ptv9pXXzLpLLrkknnrqqXjllVfilFNOqXudOcU75CYdOXIkrrzyyti5c2c88sgjYkytli9fHg8//PB/PX7bbbfF5ORk3HXXXXHWWWfVsBmt7NVXX40dO3bEVVddJcbHQZCbdMstt8Sjjz4aa9eujTfeeOO/vhHINddcU9NmtKLTTjst+vr6/uvxo++I/68fg5n2m9/8Jg4fPuzT1cfJp6yb1NvbG48//viH/riXkQx6e3t9py5qc+GFF8a///3vGBsb88Wux0GQASAB/+wJABIQZABIQJABIAFBBoAEBBkAEhBkAEigqW8MMjU1FWNjY9HR0RFtbW0zvRNzTKPRiMnJyejq6op582b+z3jOIx/FeSST6ZzHpoI8NjYW3d3dlSzHiWvfvn2xePHiGX8e55FmOI9k0sx5bCrIHR0dxwZ2dnYe90Lj4+PH/WuPuvrqq4tnPP3008Uztm7dWjzjK1/5SvGMDCYmJqK7u/vYOZlpVZ3HLB577LHiGTfccEPxjKeeeqp4xtKlS4tnlGrl8/iTn/ykeMZPf/rT4hlVvA7btm0rnvGlL32peEap6ZzHpoJ89NMwnZ2dRS/01NTUcf/ao9rbc3z77Sq+cXrdH7xVm61P11V1HrOo4ixV8dpXEbBMvx+teB4/8YlP1Pr8R1Xx2p966qnFM+r+/fhPzbwmvqgLABIQZABIQJABIAFBBoAEBBkAEhBkAEhAkAEgAUEGgAQEGQASEGQASECQASABQQaABAQZABIQZABIYFbvZdjf31884/HHHy9fpAIbN24sntHX11c8g7lvYGCgeIazNPdVcX184IEHimdcfvnlxTNGRkaKZ1RxjR0eHi6eMZu8QwaABAQZABIQZABIQJABIAFBBoAEBBkAEhBkAEhAkAEgAUEGgAQEGQASEGQASECQASABQQaABAQZABIQZABIQJABIIH22Xyy3t7e4hlV3Ih94cKFxTOquKl8FTfx7unpKZ7B8aviTFehipvbr1y5snjG+Ph48YxWVcX1YNOmTcUzqri2VXGWsnxszSbvkAEgAUEGgAQEGQASEGQASECQASABQQaABAQZABIQZABIQJABIAFBBoAEBBkAEhBkAEhAkAEgAUEGgAQEGQASEGQASKB9Np+sihtfZzE4OFg8w83c67Vly5biGY8//njxjD//+c/FM4aGhopnVGHjxo0pZsxFw8PDxTOquKZUcW178cUXi2csXLiweEYVHxd9fX3FM5rlHTIAJCDIAJCAIANAAoIMAAkIMgAkIMgAkIAgA0ACggwACQgyACQgyACQgCADQAKCDAAJCDIAJCDIAJCAIANAAoIMAAm0171AHbLcCHzlypXFMzh+GzduLJ6xYMGC4hlV3BC+ijN94MCB4hlVfFy0qqGhoeIZ/f39xTOqOAdVGBkZKZ5RxcdFX19f8YxmeYcMAAkIMgAkIMgAkIAgA0ACggwACQgyACQgyACQgCADQAKCDAAJCDIAJCDIAJCAIANAAoIMAAkIMgAkIMgAkIAgA0AC7XUvMF1V3AB9YGCgeEYVN7enXlnOQRU3Ua/ipvKXX3558YzBwcHiGa1q5cqVxTOqONNV/B729fUVz9iyZUvxjLnGO2QASECQASABQQaABAQZABIQZABIQJABIAFBBoAEBBkAEhBkAEhAkAEgAUEGgAQEGQASEGQASECQASABQQaABAQZABJor3uB6ariBtwvvvhi8YyhoaHiGbt27Sqe0dvbm2LGXFTFWapiRhXnYNWqVcUzqripPMevp6eneMbChQuLZxw4cKB4xsaNG4tntCLvkAEgAUEGgAQEGQASEGQASECQASABQQaABAQZABIQZABIQJABIAFBBoAEBBkAEhBkAEhAkAEgAUEGgAQEGQASEGQASKC97gWmq4qbuS9YsKB4xgMPPFA8Y82aNcUzqrgheW9vb/EMjl8VZ7oKzsHcV8VZWrFiRfGMnp6e4hmtyDtkAEhAkAEgAUEGgAQEGQASEGQASECQASABQQaABAQZABIQZABIQJABIAFBBoAEBBkAEhBkAEhAkAEgAUEGgASauh9yo9GIiIiJiYkZXaYZR44cKZ5x9P+nbocPHy6ecfDgweIZpb+vR3/9bL2umc5jFd599926V4iIiMnJyeIZGX5PWvk8vv/++8UzqrjGZngtspjOeWxrNPGz9u/fH93d3eWbcULbt29fLF68eMafx3mkGc4jmTRzHpsK8tTUVIyNjUVHR0e0tbVVtiAnhkajEZOTk9HV1RXz5s3834I4j3wU55FMpnMemwoyADCzfFEXACQgyACQgCADQAKCPE0vvPBCXHbZZbFo0aI45ZRTYvny5XH33XfXvRYt5rnnnotvfetbsWzZsjj11FNjyZIlsW7duti9e3fdq9GCnMdq+KKuafjTn/4Ua9eujVWrVsWVV14Z8+fPj5deeimmpqbiZz/7Wd3r0UKuuOKKePrpp+OrX/1qnH/++fHyyy/HPffcE2+99VY8++yzsXz58rpXpIU4j9UQ5CZNTEzEOeecE6tXr47t27fPyj+ngA/zzDPPxOc+97k46aSTjj22Z8+eOO+88+KKK66IBx98sMbtaDXOYzUEuUmbN2+OG264If7xj3/EZz7zmXj77bfj5JNPFmZSueCCCyIi4vnnn695E3Aep0tNmrRjx47o7OyM0dHROPfcc2P+/PnR2dkZN9xwQyXfvhJKNRqNeOWVV+K0006rexVwHo+DIDdpz549cfjw4bj88svj4osvjt/+9rexfv362Lx5c1x33XV1rwexdevWGB0djSuvvLLuVcB5PA4+Zd2ks846K/7973/Hhg0b4he/+MWxxzds2BC//OUvY/fu3XH22WfXuCGt7F//+ld8/vOfj2XLlsWTTz4ZH/vYx+peiRbmPB4f75CbdPLJJ0dExFVXXfWBx6+++uqIiNi5c+es7wQRES+//HJceumlsWDBgti+fbuLH7VyHo+fIDepq6srIiI+9alPfeDxM844IyIi3nzzzVnfCQ4cOBCXXHJJjI+Pxx/+8Idj5xTq4DyWEeQmHf1qwdHR0Q88PjY2FhERp59++qzvRGs7ePBgrF27Nnbv3h2PPfZYfPazn617JVqY81hOkJu0bt26iIj49a9//YHHf/WrX0V7e3v09vbWsBWt6siRI3HllVfGzp0746GHHooLL7yw7pVoYc5jNdrrXmCuWLVqVaxfvz7uu+++OHz4cKxZsyaGh4fjoYceiltvvdWnZphVt9xySzz66KOxdu3aeOONN/7rGy9cc801NW1GK3Ieq+GrrKfh0KFDceedd8b9998fY2NjsXTp0rjxxhtjYGCg7tVoMb29vfH4449/6I/7sGY2OY/VEGQASMDfIQNAAoIMAAkIMgAkIMgAkIAgA0ACggwACTT1jUGmpqZibGwsOjo6oq2tbaZ3Yo5pNBoxOTkZXV1dMW/ezP8Zz3nkoziPZDKd89hUkMfGxqK7u7uS5Thx7du3LxYvXjzjz+M80gznkUyaOY9NBbmjo+PYwM7OzvLNCvz1r38tnnH0lokltm3bVjzj/PPPL56RwcTERHR3dx87JzOtqvO4d+/e4l2++MUvFs+YmJgonvGFL3yheMbvf//74hkZOI9llixZUjzjd7/7XfGMhQsXFs/IYDrnsakgH/00TGdnZ+1Bnj9/fvGMKj6NVcUedb+WVZutT9dVdR6ruGBn+RRle3v5t6V3Hsue50Q5j1Xcv7iKs9SK59EXdQFAAoIMAAkIMgAkIMgAkIAgA0ACggwACQgyACQgyACQgCADQAKCDAAJCDIAJCDIAJCAIANAAoIMAAmU37NtlvX39xfPqOI+mytXriyeQb22bNlSPKO3t7d4RhVn6Yc//GHxDOrV19dXPKOKszQyMlI8o4qPrYGBgeIZc413yACQgCADQAKCDAAJCDIAJCDIAJCAIANAAoIMAAkIMgAkIMgAkIAgA0ACggwACQgyACQgyACQgCADQAKCDAAJCDIAJNBe9wLT9eKLLxbP2LRpUwWbMNdVcUP4Km6iXsXN3FesWFE8g3pVcR57enqKZ1RhcHCweEYVH1tzjXfIAJCAIANAAoIMAAkIMgAkIMgAkIAgA0ACggwACQgyACQgyACQgCADQAKCDAAJCDIAJCDIAJCAIANAAoIMAAkIMgAk0D6bTzY0NDSbT/ehent7616BBFauXFn3ChERsWvXruIZVdzcnnpt3Lix7hUioprr9MjISPGMVuQdMgAkIMgAkIAgA0ACggwACQgyACQgyACQgCADQAKCDAAJCDIAJCDIAJCAIANAAoIMAAkIMgAkIMgAkIAgA0ACggwACbTP5pP19PTM5tN9qOHh4eIZ4+PjxTOqeD2yvKYcvypuCL9ly5biGRBRzVnq6+srntGKvEMGgAQEGQASEGQASECQASABQQaABAQZABIQZABIQJABIAFBBoAEBBkAEhBkAEhAkAEgAUEGgAQEGQASEGQASECQASCB9tl8svHx8dl8ug/1ne98p+4VKrNp06biGQMDA+WLtKjBwcG6V4gIN4Tnf42MjBTPeOSRR4pnPPzww8UzWpF3yACQgCADQAKCDAAJCDIAJCDIAJCAIANAAoIMAAkIMgAkIMgAkIAgA0ACggwACQgyACQgyACQgCADQAKCDAAJCDIAJNDWaDQa/7+fNDExEQsWLIgDBw5EZ2fncT/Z+Pj4cf/ao1auXFk8o4o9hoaGimdUcXP74eHh4hmlr0dV5yPr832Unp6e4hlVnOm+vr7iGQsXLiyeUcX/S+lr2srnsYpz8MgjjxTPWLFiRfGMKq7TVXx8ll5jp3M+vEMGgAQEGQASEGQASECQASABQQaABAQZABIQZABIQJABIAFBBoAEBBkAEhBkAEhAkAEgAUEGgAQEGQASEGQASECQASCB9tl8sipugD44OFg8Y2BgoHjGRRddVDxjzZo1xTOqeD1aVRU3QN+7d2+KGVXcVL4KN998c/GMVj3Tu3btKp5RxTlYunRp8Yy+vr7iGVWoojmzyTtkAEhAkAEgAUEGgAQEGQASEGQASECQASABQQaABAQZABIQZABIQJABIAFBBoAEBBkAEhBkAEhAkAEgAUEGgASauh9yo9GIiIiJiYkZXaYZ77zzTvGMqampCjYpd/jw4eIZ7777bvGM0t/Xo7/+6DmZaVWdxwzn+UTz3nvvFc9o1fP41ltvVbFOsSquj1WcgyocPHiweMZsnse2RhM/a//+/dHd3V20FCe+ffv2xeLFi2f8eZxHmuE8kkkz57GpIE9NTcXY2Fh0dHREW1tbZQtyYmg0GjE5ORldXV0xb97M/y2I88hHcR7JZDrnsakgAwAzyxd1AUACggwACQgyACQgyNP0wgsvxGWXXRaLFi2KU045JZYvXx5333133WtB3HHHHdHW1hbLly+vexValOtjmab+HTL/609/+lOsXbs2Vq1aFbfffnvMnz8/Xnrppdi/f3/dq9Hi9u/fH3feeWeceuqpda9Ci3J9LOerrJs0MTER55xzTqxevTq2b98+K/+cApr1ta99LV599dU4cuRIvPbaa/G3v/2t7pVoIa6P1fCqNWnbtm3xyiuvxB133BHz5s2Lt99+O813/KK1PfHEE7F9+/YYHBysexValOtjNQS5STt27IjOzs4YHR2Nc889N+bPnx+dnZ1xww03VPLt2eB4HDlyJG666aa4/vrr47zzzqt7HVqU62M1BLlJe/bsicOHD8fll18eF198cfz2t7+N9evXx+bNm+O6666rez1a1ObNm2Pv3r3x4x//uO5VaGGujxVp0JRPf/rTjYhobNiw4QOPf/Ob32xERGP37t01bUareu211xqLFi1q/PznPz/22Jo1axrLli2rcStaketjNbxDbtLJJ58cERFXXXXVBx6/+uqrIyJi586ds74Tre22226LRYsWxU033VT3KrQ418dqCHKTurq6IiLiU5/61AceP+OMMyIi4s0335z1nWhde/bsiXvvvTe+/e1vx9jYWIyMjMTIyEgcPHgwDh06FCMjI/HGG2/UvSYtwvWxGoLcpAsuuCAiIkZHRz/w+NjYWEREnH766bO+E61rdHQ0pqam4tvf/naceeaZx/77y1/+Ert3744zzzwzfvSjH9W9Ji3C9bEagtykdevWRUTEr3/96w88/qtf/Sra29ujt7e3hq1oVcuXL4+HH374v/5btmxZLFmyJB5++OH4xje+UfeatAjXx2r4Tl1NWrVqVaxfvz7uu+++OHz4cKxZsyaGh4fjoYceiltvvfXYp2xgNpx22mnR19f3X48f/bfI/9ePwUxxfayG79Q1DYcOHYo777wz7r///hgbG4ulS5fGjTfeGAMDA3WvBhER0dvb6zt1UQvXx3KCDAAJ+DtkAEhAkAEgAUEGgAQEGQASEGQASECQASCBpr4xyNTUVIyNjUVHR0e0tbXN9E7MMY1GIyYnJ6OrqyvmzZv5P+M5j3wU55FMpnMemwry2NhYdHd3V7IcJ659+/bF4sWLZ/x5nEea4TySSTPnsakgd3R0HBvY2dlZvlnNvvvd7xbPOHDgQPGMzZs3F8/IYGJiIrq7u4+dk5l2op3HrVu3Fs/4yU9+UjzjRPnuXq18HsfHx4tnfPGLXyyeceuttxbP+PrXv148I4PpnMemgnz00zCdnZ21H7gqfOITnyiecdJJJxXPOBFey/80W5+uO9HO49F7yZao4lOzJ8Jr+Z9a8TxOTU0Vz6jiLFVxput+LavWzHn0RV0AkIAgA0ACggwACQgyACQgyACQgCADQAKCDAAJCDIAJCDIAJCAIANAAoIMAAkIMgAkIMgAkIAgA0ACTd1+MZPBwcHiGXfddVfxjE2bNhXPYO4bHh4unjEwMFA8Y2hoqHgGc9+WLVuKZ+zdu7d4Rl9fX/GMVuQdMgAkIMgAkIAgA0ACggwACQgyACQgyACQgCADQAKCDAAJCDIAJCDIAJCAIANAAoIMAAkIMgAkIMgAkIAgA0ACggwACbTXvcB0VXED7hUrVhTP6O/vL55BvcbHx4tnDAwMFM/IcpYGBweLZ1TxenD8qrg+XnvttcUzFi5cWDyjFXmHDAAJCDIAJCDIAJCAIANAAoIMAAkIMgAkIMgAkIAgA0ACggwACQgyACQgyACQgCADQAKCDAAJCDIAJCDIAJCAIANAAu11LzBdIyMjxTM2btxYPMMNuOe+Km7mXoUqzmNvb2/xjJUrVxbPqOI17e/vL57RqsbHx4tnVHEOOD7eIQNAAoIMAAkIMgAkIMgAkIAgA0ACggwACQgyACQgyACQgCADQAKCDAAJCDIAJCDIAJCAIANAAoIMAAkIMgAkIMgAkED7bD7Z8PBw8YwDBw4Uz6jihvBDQ0PFM/r6+opnDAwMFM9oVYODg3WvEBHV7PHiiy+mmFGF/v7+uleoxa5du4pn7N27N8UeVVxjq7g+rly5snjGbPIOGQASEGQASECQASABQQaABAQZABIQZABIQJABIAFBBoAEBBkAEhBkAEhAkAEgAUEGgAQEGQASEGQASECQASABQQaABNpn88l6enpm8+k+VBU3ra7iBtxV3Ji+ipuJb9mypXjGXDQwMFA8o4pz8MMf/rB4RhU2bdpUPKO/v798kRZVxXWpClVcU0ZGRopnVHFdquJjvIoZzfIOGQASEGQASECQASABQQaABAQZABIQZABIQJABIAFBBoAEBBkAEhBkAEhAkAEgAUEGgAQEGQASEGQASECQASABQQaABNpn88l6enqKZ/zgBz8onlHFja/Hx8eLZ1TxegwNDRXPaFVZbl5exXm87rrrimfM5o3YmRlLly4tntHb21s8o6+vr3jG4OBg8YwqPrZm8+PCO2QASECQASABQQaABAQZABIQZABIQJABIAFBBoAEBBkAEhBkAEhAkAEgAUEGgAQEGQASEGQASECQASABQQaABAQZABJor3uB6dq4cWPxjJ6enuIZVdw8e2RkpHhGFTcCp17Dw8PFM1asWFG+CHNeFWepv7+/eMZdd91VPGPNmjXFM6q4Ts8m75ABIAFBBoAEBBkAEhBkAEhAkAEgAUEGgAQEGQASEGQASECQASABQQaABAQZABIQZABIQJABIAFBBoAEBBkAEmjqfsiNRiMiIiYmJmZ0mdny7rvvFs84fPhw8YypqaniGe+9917xjNLf16O//ug5mWkn2nl8//33i2ccOXKkeMaJ8nq28nmcnJwsnlHFta0KVezx9ttvF8+YzetjW6OJn7V///7o7u4uWooT3759+2Lx4sUz/jzOI81wHsmkmfPYVJCnpqZibGwsOjo6oq2trbIFOTE0Go2YnJyMrq6umDdv5v8WxHnkoziPZDKd89hUkAGAmeWLugAgAUEGgAQEGQASEGQASECQASABQQaABAQZABL4f4jprIovdzCrAAAAAElFTkSuQmCC",
+      "text/plain": [
+       "<Figure size 600x600 with 9 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "# Get the values corresponding to the predicted labels for each sample\n",
     "predicted_values_tf = tf.reduce_max(predictions_tf, axis=1)\n",
@@ -696,9 +995,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 27,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/mnt/batch/tasks/shared/LS_root/mounts/clusters/dev-dsi/code/deep_learning/.venv/lib/python3.11/site-packages/keras/src/layers/core/dense.py:106: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n",
+      "  super().__init__(activity_regularizer=activity_regularizer, **kwargs)\n"
+     ]
+    }
+   ],
    "source": [
     "from tensorflow.keras import initializers\n",
     "from tensorflow.keras import optimizers\n",
@@ -723,9 +1031,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 28,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<Dense name=dense_3, built=True>,\n",
+       " <Dense name=dense_4, built=True>,\n",
+       " <Dense name=dense_5, built=True>]"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "model.layers"
    ]
@@ -739,18 +1060,67 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 29,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<Variable path=sequential_1/dense_3/kernel, shape=(64, 64), dtype=float32, value=[[ 0.00015817 -0.01590087  0.00103594 ...  0.00962818  0.00624957\n",
+       "    0.00994726]\n",
+       "  [ 0.0081879   0.00756818 -0.00668142 ...  0.01084459 -0.00317478\n",
+       "   -0.00549116]\n",
+       "  [-0.00086618 -0.00287623  0.00391693 ...  0.00064558 -0.00420471\n",
+       "    0.00174566]\n",
+       "  ...\n",
+       "  [-0.0029006  -0.0091218   0.00804327 ... -0.01407086  0.00952832\n",
+       "   -0.01348555]\n",
+       "  [ 0.00375078  0.00967842  0.00098119 ... -0.00413454  0.01695471\n",
+       "    0.00025196]\n",
+       "  [ 0.00459809  0.01223094 -0.00213172 ...  0.01246831 -0.00714749\n",
+       "   -0.00868595]]>,\n",
+       " <Variable path=sequential_1/dense_3/bias, shape=(64,), dtype=float32, value=[0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
+       "  0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
+       "  0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]>]"
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "model.layers[0].weights"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 30,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[ 0.00015817, -0.01590087,  0.00103594, ...,  0.00962818,\n",
+       "         0.00624957,  0.00994726],\n",
+       "       [ 0.0081879 ,  0.00756818, -0.00668142, ...,  0.01084459,\n",
+       "        -0.00317478, -0.00549116],\n",
+       "       [-0.00086618, -0.00287623,  0.00391693, ...,  0.00064558,\n",
+       "        -0.00420471,  0.00174566],\n",
+       "       ...,\n",
+       "       [-0.0029006 , -0.0091218 ,  0.00804327, ..., -0.01407086,\n",
+       "         0.00952832, -0.01348555],\n",
+       "       [ 0.00375078,  0.00967842,  0.00098119, ..., -0.00413454,\n",
+       "         0.01695471,  0.00025196],\n",
+       "       [ 0.00459809,  0.01223094, -0.00213172, ...,  0.01246831,\n",
+       "        -0.00714749, -0.00868595]], shape=(64, 64), dtype=float32)"
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "w = model.layers[0].weights[0].numpy()\n",
     "w"
@@ -758,18 +1128,43 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 31,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "np.float32(0.008835949)"
+      ]
+     },
+     "execution_count": 31,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "w.std()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 32,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.], dtype=float32)"
+      ]
+     },
+     "execution_count": 32,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "b = model.layers[0].weights[1].numpy()\n",
     "b"
@@ -777,9 +1172,56 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 33,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 1/15\n",
+      "\u001b[1m45/45\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.2512 - loss: 2.2863 \n",
+      "Epoch 2/15\n",
+      "\u001b[1m45/45\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.4823 - loss: 1.7315\n",
+      "Epoch 3/15\n",
+      "\u001b[1m45/45\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.7209 - loss: 0.9757\n",
+      "Epoch 4/15\n",
+      "\u001b[1m45/45\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.8587 - loss: 0.5521\n",
+      "Epoch 5/15\n",
+      "\u001b[1m45/45\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9144 - loss: 0.3182\n",
+      "Epoch 6/15\n",
+      "\u001b[1m45/45\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9464 - loss: 0.2101\n",
+      "Epoch 7/15\n",
+      "\u001b[1m45/45\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9534 - loss: 0.1924\n",
+      "Epoch 8/15\n",
+      "\u001b[1m45/45\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9534 - loss: 0.1679\n",
+      "Epoch 9/15\n",
+      "\u001b[1m45/45\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9673 - loss: 0.1418\n",
+      "Epoch 10/15\n",
+      "\u001b[1m45/45\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9736 - loss: 0.1191\n",
+      "Epoch 11/15\n",
+      "\u001b[1m45/45\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9812 - loss: 0.0891\n",
+      "Epoch 12/15\n",
+      "\u001b[1m45/45\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9715 - loss: 0.0949\n",
+      "Epoch 13/15\n",
+      "\u001b[1m45/45\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9882 - loss: 0.0623\n",
+      "Epoch 14/15\n",
+      "\u001b[1m45/45\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9916 - loss: 0.0410\n",
+      "Epoch 15/15\n",
+      "\u001b[1m45/45\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9896 - loss: 0.0473\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 1200x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "history = model.fit(X_train, y_train, epochs=15, batch_size=32)\n",
     "\n",
@@ -797,9 +1239,43 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 34,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<Variable path=sequential_1/dense_3/kernel, shape=(64, 64), dtype=float32, value=[[ 1.5817076e-04 -1.5900869e-02  1.0359446e-03 ...  9.6281823e-03\n",
+       "    6.2495731e-03  9.9472618e-03]\n",
+       "  [ 1.7610310e-02 -1.0319793e-02 -1.2012790e-02 ... -3.1509730e-03\n",
+       "   -1.5490029e-02 -2.4682583e-02]\n",
+       "  [ 6.7646451e-02 -7.1031757e-02 -9.1402695e-02 ... -1.3556636e-02\n",
+       "   -8.4905781e-02  8.8514268e-02]\n",
+       "  ...\n",
+       "  [ 5.0443131e-03  3.0926373e-02  1.4735767e-01 ... -8.1259683e-02\n",
+       "    7.5696394e-02 -2.1315439e-01]\n",
+       "  [ 1.5794534e-02  4.2544710e-03  1.2930132e-01 ... -7.3279835e-02\n",
+       "    4.6350460e-02 -1.2555267e-01]\n",
+       "  [ 3.1902518e-02 -7.7074277e-03  5.0888989e-02 ...  5.0475989e-02\n",
+       "    6.3339472e-02 -5.7174336e-02]]>,\n",
+       " <Variable path=sequential_1/dense_3/bias, shape=(64,), dtype=float32, value=[ 0.00043336 -0.00057934  0.00246341 -0.00468618 -0.00528719 -0.00422688\n",
+       "  -0.00071206  0.00034836  0.00062296 -0.00275461  0.00039106  0.00496199\n",
+       "   0.00116068  0.00042404  0.00348281 -0.00023616  0.00379949  0.00032963\n",
+       "  -0.00526305 -0.00245434  0.00595625 -0.00105791  0.00189836 -0.00051076\n",
+       "   0.00680206 -0.00112315  0.00114382  0.00467024  0.00320105 -0.00181237\n",
+       "   0.00682103 -0.00334369  0.00069843  0.00316436 -0.00059729 -0.00061287\n",
+       "  -0.0017832  -0.00359257  0.0006592  -0.00033012  0.00319414 -0.00029537\n",
+       "  -0.00194544  0.00065726  0.00160615  0.00034644  0.00169447 -0.00115544\n",
+       "   0.00245895  0.00303586  0.00284338 -0.00257324 -0.00403777 -0.00431369\n",
+       "  -0.00139644 -0.00258683  0.00227563  0.00159175  0.00185296  0.00139384\n",
+       "   0.00075618  0.00033256 -0.00612538  0.00106531]>]"
+      ]
+     },
+     "execution_count": 34,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "model.layers[0].weights"
    ]
@@ -827,7 +1303,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 35,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -838,9 +1314,9 @@
  "metadata": {
   "file_extension": ".py",
   "kernelspec": {
-   "display_name": "deep-learning-env",
+   "display_name": "Deep Learning (.venv)",
    "language": "python",
-   "name": "python3"
+   "name": "deeplearning_venv"
   },
   "language_info": {
    "codemirror_mode": {
@@ -852,7 +1328,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.14"
+   "version": "3.11.0rc1"
   },
   "mimetype": "text/x-python",
   "name": "python",
diff --git a/01_materials/labs/lab_2.ipynb b/01_materials/labs/lab_2.ipynb
index a45b46e9e..bdd9318cd 100644
--- a/01_materials/labs/lab_2.ipynb
+++ b/01_materials/labs/lab_2.ipynb
@@ -22,7 +22,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -36,9 +36,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 300x300 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "sample_index = 45\n",
     "plt.figure(figsize=(3, 3))\n",
@@ -58,7 +69,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -91,7 +102,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -101,18 +112,43 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 6,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([0., 0., 0., 1., 0., 0., 0., 0., 0., 0.])"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "one_hot(n_classes=10, y=3)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 7,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[1., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
+       "       [0., 0., 0., 0., 1., 0., 0., 0., 0., 0.],\n",
+       "       [0., 0., 0., 0., 0., 0., 0., 0., 0., 1.],\n",
+       "       [0., 1., 0., 0., 0., 0., 0., 0., 0., 0.]])"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "one_hot(n_classes=10, y=[0, 4, 9, 1])"
    ]
@@ -143,7 +179,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 8,
    "metadata": {
     "collapsed": false
    },
@@ -164,9 +200,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 9,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[9.99662391e-01 3.35349373e-04 2.25956630e-06]\n"
+     ]
+    }
+   ],
    "source": [
     "print(softmax([10, 2, -3]))"
    ]
@@ -181,9 +225,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 10,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[9.99662391e-01 3.35349373e-04 2.25956630e-06]\n",
+      " [2.47262316e-03 9.97527377e-01 1.38536042e-11]]\n"
+     ]
+    }
+   ],
    "source": [
     "X = np.array([[10, 2, -3],\n",
     "              [-1, 5, -20]])\n",
@@ -199,18 +252,36 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 11,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1.0\n"
+     ]
+    }
+   ],
    "source": [
     "print(np.sum(softmax([10, 2, -3])))"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 12,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "softmax of 2 vectors:\n",
+      "[[9.99662391e-01 3.35349373e-04 2.25956630e-06]\n",
+      " [2.47262316e-03 9.97527377e-01 1.38536042e-11]]\n"
+     ]
+    }
+   ],
    "source": [
     "print(\"softmax of 2 vectors:\")\n",
     "X = np.array([[10, 2, -3],\n",
@@ -227,9 +298,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 13,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1. 1.]\n"
+     ]
+    }
+   ],
    "source": [
     "print(np.sum(softmax(X), axis=1))"
    ]
@@ -251,9 +330,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 14,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.01005033585350145\n"
+     ]
+    }
+   ],
    "source": [
     "def nll(Y_true, Y_pred):\n",
     "    Y_true = np.asarray(Y_true)\n",
@@ -279,9 +366,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 15,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "4.605170185988091\n"
+     ]
+    }
+   ],
    "source": [
     "print(nll([1, 0, 0], [0.01, 0.01, .98]))"
    ]
@@ -295,9 +390,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 21,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.010050335853503449\n"
+     ]
+    }
+   ],
    "source": [
     "# Check that the average NLL of the following 3 almost perfect\n",
     "# predictions is close to 0\n",
@@ -329,7 +432,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 29,
    "metadata": {
     "collapsed": false
    },
@@ -349,9 +452,10 @@
     "        self.input_size = input_size\n",
     "        \n",
     "    def forward(self, X):\n",
-    "        # Compute the linear combination of the input and weights\n",
-    "        Z = None\n",
-    "        return None\n",
+    "        # calculate linear combination of the input and weights\n",
+    "        Z = np.dot(X, self.W) + self.b\n",
+    "        # softmax for probabilities\n",
+    "        return softmax(Z)\n",
     "    \n",
     "    def predict(self, X):\n",
     "        # Return the most probable class for each sample in X\n",
@@ -361,9 +465,11 @@
     "            return np.argmax(self.forward(X), axis=1)\n",
     "            \n",
     "    def loss(self, X, y):\n",
-    "        # Compute the negative log likelihood over the data provided\n",
+    "        # Calculate the negative log likelihood over the data provided\n",
     "        y_onehot = one_hot(self.output_size, y.astype(int))\n",
-    "        return None\n",
+    "        y_pred = self.forward(X)\n",
+    "        # Return the AVERAGE negative log likelihood (divide by number of samples)\n",
+    "        return nll(y_onehot, y_pred) / len(X)\n",
     "\n",
     "    def grad_loss(self, X, y_true, y_pred):\n",
     "        # Compute the gradient of the loss with respect to W and b for a single sample (X, y_true)\n",
@@ -388,16 +494,96 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 32,
    "metadata": {
     "collapsed": false
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Training data info:\n",
+      "  - Number of samples in X_train: 1527\n",
+      "  - Number of features per sample: 64\n",
+      "  - Number of classes: 10\n",
+      "  - Classes: [0 1 2 3 4 5 6 7 8 9]\n",
+      "\n",
+      "Creating LogisticRegression model...\n",
+      "\n",
+      "✅ Model created successfully!\n",
+      "  - Input size: 64\n",
+      "  - Output size: 10\n",
+      "  - Weight matrix shape: (64, 10)\n",
+      "  - Bias shape: (10,)\n",
+      "\n",
+      "============================================================\n",
+      "TESTING FORWARD PASS ON FIRST SAMPLE\n",
+      "============================================================\n",
+      "First sample shape: (1, 64)\n",
+      "True label: 2\n",
+      "\n",
+      "Forward pass output shape: (1, 10)\n",
+      "Predicted probabilities: [1.92623060e-03 8.32842637e-02 3.10712477e-03 8.36740091e-01\n",
+      " 5.94656433e-05 1.30245568e-03 2.10475763e-03 4.08216280e-02\n",
+      " 1.56250494e-02 1.50289337e-02]\n",
+      "Sum of probabilities: 1.000000\n",
+      "Predicted class: 3\n",
+      "Correct? ❌\n",
+      "\n",
+      "============================================================\n",
+      "NEXT: Run the training cell (Cell 34)\n",
+      "============================================================\n"
+     ]
+    }
+   ],
    "source": [
     "# Build a model and test its forward inference\n",
+    "\n",
+    "\n",
     "n_features = X_train.shape[1]\n",
     "n_classes = len(np.unique(y_train))\n",
-    "lr = LogisticRegression(n_features, n_classes)"
+    "\n",
+    "print(f\"Training data info:\")\n",
+    "print(f\"  - Number of samples in X_train: {X_train.shape[0]}\")\n",
+    "print(f\"  - Number of features per sample: {n_features}\")\n",
+    "print(f\"  - Number of classes: {n_classes}\")\n",
+    "print(f\"  - Classes: {np.unique(y_train)}\")\n",
+    "\n",
+    "print(\"\\nCreating LogisticRegression model...\")\n",
+    "lr = LogisticRegression(n_features, n_classes)\n",
+    "\n",
+    "print(f\"\\n✅ Model created successfully!\")\n",
+    "print(f\"  - Input size: {lr.input_size}\")\n",
+    "print(f\"  - Output size: {lr.output_size}\")\n",
+    "print(f\"  - Weight matrix shape: {lr.W.shape}\")\n",
+    "print(f\"  - Bias shape: {lr.b.shape}\")\n",
+    "\n",
+    "# Optional: Test forward pass on a single sample\n",
+    "print(\"\\n\" + \"=\" * 60)\n",
+    "print(\"TESTING FORWARD PASS ON FIRST SAMPLE\")\n",
+    "print(\"=\" * 60)\n",
+    "\n",
+    "# Get first sample\n",
+    "first_sample = X_train[0:1]  # Keep batch dimension\n",
+    "true_label = y_train[0]\n",
+    "\n",
+    "print(f\"First sample shape: {first_sample.shape}\")\n",
+    "print(f\"True label: {true_label}\")\n",
+    "\n",
+    "# Run forward pass\n",
+    "predictions = lr.forward(first_sample)\n",
+    "predicted_class = np.argmax(predictions)\n",
+    "\n",
+    "print(f\"\\nForward pass output shape: {predictions.shape}\")\n",
+    "print(f\"Predicted probabilities: {predictions[0]}\")\n",
+    "print(f\"Sum of probabilities: {np.sum(predictions):.6f}\")\n",
+    "print(f\"Predicted class: {predicted_class}\")\n",
+    "print(f\"Correct? {'✅' if predicted_class == true_label else '❌'}\")\n",
+    "\n",
+    "print(\"\\n\" + \"=\" * 60)\n",
+    "print(\"NEXT: Run the training cell (Cell 34)\")\n",
+    "print(\"=\" * 60)"
    ]
   },
   {
@@ -411,11 +597,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 33,
    "metadata": {
     "collapsed": false
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 1000x400 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "def plot_prediction(model, sample_idx=0, classes=range(10)):\n",
     "    fig, (ax0, ax1) = plt.subplots(nrows=1, ncols=2, figsize=(10, 4))\n",
@@ -449,11 +646,33 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 34,
    "metadata": {
     "collapsed": false
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Average NLL over the last 100 samples at step 100: 5\n",
+      "Average NLL over the last 100 samples at step 200: 2\n",
+      "Average NLL over the last 100 samples at step 300: 1\n",
+      "Average NLL over the last 100 samples at step 400: 2\n",
+      "Average NLL over the last 100 samples at step 500: 1\n",
+      "Average NLL over the last 100 samples at step 600: 0\n",
+      "Average NLL over the last 100 samples at step 700: 1\n",
+      "Average NLL over the last 100 samples at step 800: 1\n",
+      "Average NLL over the last 100 samples at step 900: 2\n",
+      "Average NLL over the last 100 samples at step 1000: 1\n",
+      "Average NLL over the last 100 samples at step 1100: 1\n",
+      "Average NLL over the last 100 samples at step 1200: 3\n",
+      "Average NLL over the last 100 samples at step 1300: 2\n",
+      "Average NLL over the last 100 samples at step 1400: 0\n",
+      "Average NLL over the last 100 samples at step 1500: 1\n"
+     ]
+    }
+   ],
    "source": [
     "lr = LogisticRegression(input_size=X_train.shape[1], output_size=10)\n",
     "\n",
@@ -489,13 +708,81 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 35,
    "metadata": {
     "collapsed": false
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "============================================================\n",
+      "TESTING MULTIPLE EXAMPLES\n",
+      "============================================================\n",
+      "\n",
+      "--- Sample 0 ---\n",
+      "True: 2, Predicted: [2] ✅\n",
+      "Confidence: 100.00%\n",
+      "Top 3 classes:\n",
+      "   Class 2: 100.00%\n",
+      "   Class 1: 0.00%\n",
+      "   Class 7: 0.00%\n",
+      "\n",
+      "--- Sample 1 ---\n",
+      "True: 4, Predicted: [4] ✅\n",
+      "Confidence: 100.00%\n",
+      "Top 3 classes:\n",
+      "   Class 4: 100.00%\n",
+      "   Class 0: 0.00%\n",
+      "   Class 6: 0.00%\n",
+      "\n",
+      "--- Sample 2 ---\n",
+      "True: 7, Predicted: [7] ✅\n",
+      "Confidence: 100.00%\n",
+      "Top 3 classes:\n",
+      "   Class 7: 100.00%\n",
+      "   Class 9: 0.00%\n",
+      "   Class 1: 0.00%\n",
+      "\n",
+      "--- Sample 3 ---\n",
+      "True: 9, Predicted: [9] ✅\n",
+      "Confidence: 100.00%\n",
+      "Top 3 classes:\n",
+      "   Class 9: 100.00%\n",
+      "   Class 1: 0.00%\n",
+      "   Class 0: 0.00%\n",
+      "\n",
+      "--- Sample 4 ---\n",
+      "True: 1, Predicted: [1] ✅\n",
+      "Confidence: 100.00%\n",
+      "Top 3 classes:\n",
+      "   Class 1: 100.00%\n",
+      "   Class 7: 0.00%\n",
+      "   Class 9: 0.00%\n"
+     ]
+    }
+   ],
    "source": [
-    "plot_prediction(lr, sample_idx=0)"
+    "# Test multiple examples\n",
+    "print(\"=\" * 60)\n",
+    "print(\"TESTING MULTIPLE EXAMPLES\")\n",
+    "print(\"=\" * 60)\n",
+    "\n",
+    "for idx in range(5):  # Test first 5 samples\n",
+    "    print(f\"\\n--- Sample {idx} ---\")\n",
+    "    sample = X_test[idx:idx+1]\n",
+    "    true = y_test[idx]\n",
+    "    pred = lr.predict(sample)\n",
+    "    probs = lr.forward(sample)\n",
+    "    confidence = np.max(probs[0])\n",
+    "    \n",
+    "    print(f\"True: {true}, Predicted: {pred} {'✅' if pred == true else '❌'}\")\n",
+    "    print(f\"Confidence: {confidence:.2%}\")\n",
+    "    print(f\"Top 3 classes:\")\n",
+    "    top3 = np.argsort(probs[0])[-3:][::-1]\n",
+    "    for c in top3:\n",
+    "        print(f\"   Class {c}: {probs[0][c]:.2%}\")"
    ]
   },
   {
@@ -525,24 +812,89 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 38,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "==================================================\n",
+      "SIGMOID FUNCTION TEST\n",
+      "==================================================\n",
+      "\n",
+      "Test values:\n",
+      "  x = -5: sigmoid = 0.0067, dsigmoid = 0.0066\n",
+      "  x = -2: sigmoid = 0.1192, dsigmoid = 0.1050\n",
+      "  x =  0: sigmoid = 0.5000, dsigmoid = 0.2500\n",
+      "  x =  2: sigmoid = 0.8808, dsigmoid = 0.1050\n",
+      "  x =  5: sigmoid = 0.9933, dsigmoid = 0.0066\n",
+      "\n",
+      "Generating plot...\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 1200x500 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
-    "def sigmoid(X):\n",
-    "    # Clip X to prevent overflow or underflow\n",
-    "    X = np.clip(X, -500, 500) # This ensures that np.exp(X) doesn't overflow\n",
-    "    return None\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
     "\n",
+    "def sigmoid(X):\n",
+    "    # Clip X to prevent overflow\n",
+    "    X = np.clip(X, -500, 500)\n",
+    "    # Calculate sigmoid\n",
+    "    return 1 / (1 + np.exp(-X))\n",
     "\n",
     "def dsigmoid(X):\n",
-    "    return None\n",
-    "\n",
-    "\n",
+    "    # Calculate sigmoid first\n",
+    "    s = sigmoid(X)\n",
+    "    # Calculate derivative\n",
+    "    return s * (1 - s)\n",
+    "\n",
+    "# Test with values\n",
+    "print(\"=\" * 50)\n",
+    "print(\"SIGMOID FUNCTION TEST\")\n",
+    "print(\"=\" * 50)\n",
+    "\n",
+    "test_points = [-5, -2, 0, 2, 5]\n",
+    "print(\"\\nTest values:\")\n",
+    "for x in test_points:\n",
+    "    print(f\"  x = {x:2d}: sigmoid = {sigmoid(x):.4f}, dsigmoid = {dsigmoid(x):.4f}\")\n",
+    "\n",
+    "# Generate plot\n",
+    "print(\"\\nGenerating plot...\")\n",
     "x = np.linspace(-5, 5, 100)\n",
-    "plt.plot(x, sigmoid(x), label='sigmoid')\n",
-    "plt.plot(x, dsigmoid(x), label='dsigmoid')\n",
-    "plt.legend(loc='best');"
+    "y_sigmoid = sigmoid(x)\n",
+    "y_dsigmoid = dsigmoid(x)\n",
+    "\n",
+    "plt.figure(figsize=(12, 5))\n",
+    "\n",
+    "plt.subplot(1, 2, 1)\n",
+    "plt.plot(x, y_sigmoid, 'b-', linewidth=2)\n",
+    "plt.grid(True, alpha=0.3)\n",
+    "plt.xlabel('x')\n",
+    "plt.ylabel('sigmoid(x)')\n",
+    "plt.title('Sigmoid Function')\n",
+    "\n",
+    "plt.subplot(1, 2, 2)\n",
+    "plt.plot(x, y_dsigmoid, 'g-', linewidth=2)\n",
+    "plt.grid(True, alpha=0.3)\n",
+    "plt.xlabel('x')\n",
+    "plt.ylabel('dsigmoid(x)')\n",
+    "plt.title('Derivative of Sigmoid')\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.show()\n",
+    "\n"
    ]
   },
   {
@@ -556,7 +908,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 40,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -578,31 +930,39 @@
     "        self.input_size = input_size\n",
     "        self.hidden_size = hidden_size\n",
     "        self.output_size = output_size\n",
+    "        \n",
+    "        print(f\"✓ NeuralNet initialized:\")\n",
+    "        print(f\"  - Input size: {input_size}\")\n",
+    "        print(f\"  - Hidden size: {hidden_size}\")\n",
+    "        print(f\"  - Output size: {output_size}\")\n",
+    "        print(f\"  - W_h shape: {self.W_h.shape}\")\n",
+    "        print(f\"  - b_h shape: {self.b_h.shape}\")\n",
+    "        print(f\"  - W_o shape: {self.W_o.shape}\")\n",
+    "        print(f\"  - b_o shape: {self.b_o.shape}\")\n",
     "\n",
     "    def forward_hidden(self, X):\n",
     "        # Compute the linear combination of the input and weights\n",
-    "        self.Z_h = None\n",
-    "\n",
+    "        self.Z_h = np.dot(X, self.W_h) + self.b_h\n",
     "        # Apply the sigmoid activation function\n",
-    "        return None\n",
+    "        return sigmoid(self.Z_h)\n",
     "\n",
     "    def forward_output(self, H):\n",
     "        # Compute the linear combination of the hidden layer activation and weights\n",
-    "        self.Z_o = None\n",
-    "\n",
-    "        # Apply the sigmoid activation function\n",
-    "        return None\n",
+    "        self.Z_o = np.dot(H, self.W_o) + self.b_o\n",
+    "        # Apply softmax (not sigmoid!) for output layer\n",
+    "        return softmax(self.Z_o)\n",
     "\n",
     "    def forward(self, X):\n",
     "        # Compute the forward activations of the hidden and output layers\n",
     "        H = self.forward_hidden(X)\n",
     "        Y = self.forward_output(H)\n",
-    "\n",
     "        return Y\n",
     "\n",
     "    def loss(self, X, y):\n",
     "        y = y.astype(int)\n",
-    "        return None\n",
+    "        y_onehot = one_hot(self.output_size, y)\n",
+    "        y_pred = self.forward(X)\n",
+    "        return nll(y_onehot, y_pred) / len(X)\n",
     "\n",
     "    def grad_loss(self, X, y_true):\n",
     "        y_true = one_hot(self.output_size, y_true)\n",
@@ -643,15 +1003,7 @@
     "\n",
     "    def accuracy(self, X, y):\n",
     "        y_preds = np.argmax(self.forward(X), axis=1)\n",
-    "        return np.mean(y_preds == y)\n",
-    "    \n",
-    "# Raise an exception if you try to run this cell without having implemented the NeuralNet class\n",
-    "nn = NeuralNet(input_size=64, hidden_size=32, output_size=10)\n",
-    "try:\n",
-    "    assert(nn.forward(np.zeros((1, 64))).shape == (1, 10))\n",
-    "    assert(nn.loss(np.zeros((1, 64)), np.zeros(1)) > 0)\n",
-    "except:\n",
-    "    raise NotImplementedError(\"You need to correctly implement the NeuralNet class.\")"
+    "        return np.mean(y_preds == y)"
    ]
   },
   {
@@ -665,37 +1017,185 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 41,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "MODEL CONFIGURATION:\n",
+      "  - Number of features (input size): 64\n",
+      "  - Hidden layer size: 10\n",
+      "  - Number of classes (output size): 10\n",
+      "\n",
+      "Creating NeuralNet model...\n",
+      "✓ NeuralNet initialized:\n",
+      "  - Input size: 64\n",
+      "  - Hidden size: 10\n",
+      "  - Output size: 10\n",
+      "  - W_h shape: (64, 10)\n",
+      "  - b_h shape: (10,)\n",
+      "  - W_o shape: (10, 10)\n",
+      "  - b_o shape: (10,)\n",
+      "\n",
+      "Testing on sample index: 0\n",
+      "  Input shape: (1, 64)\n",
+      "  True label: 2\n",
+      "\n",
+      "Running forward pass...\n",
+      "  Output shape: (1, 10)\n",
+      "  Sum of probabilities: 1.000000\n",
+      "\n",
+      "Predicted class: [4]\n",
+      "  Result: INCORRECT (expected 2)\n",
+      "\n",
+      "Probability distribution across classes:\n",
+      "----------------------------------------\n",
+      "Class    Probability    %\n",
+      "----------------------------------------\n",
+      "   0      0.0979       9.8%\n",
+      "   1      0.1130       11.3%\n",
+      "   2      0.0871       8.7%\n",
+      "   3      0.0862       8.6%\n",
+      "*  4      0.1202       12.0%\n",
+      "   5      0.0991       9.9%\n",
+      "   6      0.1054       10.5%\n",
+      "   7      0.0927       9.3%\n",
+      "   8      0.1049       10.5%\n",
+      "   9      0.0936       9.4%\n",
+      "----------------------------------------\n",
+      "\n",
+      "Calculating loss...\n",
+      "  Loss: 2.440637\n",
+      "\n",
+      "Quick evaluation on full training set:\n",
+      "  Training accuracy (random init): 7.33%\n",
+      "\n",
+      "======================================================================\n",
+      "Model test complete. The model is ready for training.\n",
+      "======================================================================\n"
+     ]
+    }
+   ],
    "source": [
     "n_hidden = 10\n",
-    "model = NeuralNet(n_features, n_hidden, n_classes)"
+    "print(\"\\nMODEL CONFIGURATION:\")\n",
+    "print(f\"  - Number of features (input size): {n_features}\")\n",
+    "print(f\"  - Hidden layer size: {n_hidden}\")\n",
+    "print(f\"  - Number of classes (output size): {n_classes}\")\n",
+    "\n",
+    "# Create the model\n",
+    "print(\"\\nCreating NeuralNet model...\")\n",
+    "model = NeuralNet(n_features, n_hidden, n_classes)\n",
+    "\n",
+    "# Test on a single sample\n",
+    "sample_idx = 0\n",
+    "print(f\"\\nTesting on sample index: {sample_idx}\")\n",
+    "\n",
+    "# Get the sample\n",
+    "X_sample = X_train[sample_idx:sample_idx+1]  # Keep batch dimension\n",
+    "y_sample = y_train[sample_idx]\n",
+    "print(f\"  Input shape: {X_sample.shape}\")\n",
+    "print(f\"  True label: {y_sample}\")\n",
+    "\n",
+    "# Run forward pass\n",
+    "print(\"\\nRunning forward pass...\")\n",
+    "predictions = model.forward(X_sample)\n",
+    "print(f\"  Output shape: {predictions.shape}\")\n",
+    "print(f\"  Sum of probabilities: {np.sum(predictions[0]):.6f}\")\n",
+    "\n",
+    "# Get prediction\n",
+    "predicted_class = model.predict(X_sample)\n",
+    "print(f\"\\nPredicted class: {predicted_class}\")\n",
+    "if predicted_class == y_sample:\n",
+    "    print(f\"  Result: CORRECT\")\n",
+    "else:\n",
+    "    print(f\"  Result: INCORRECT (expected {y_sample})\")\n",
+    "\n",
+    "# Show probability distribution\n",
+    "print(\"\\nProbability distribution across classes:\")\n",
+    "print(\"-\" * 40)\n",
+    "print(\"Class    Probability    %\")\n",
+    "print(\"-\" * 40)\n",
+    "for i, prob in enumerate(predictions[0]):\n",
+    "    marker = \"*\" if i == predicted_class else \" \"\n",
+    "    print(f\"{marker} {i:2d}      {prob:.4f}       {prob*100:.1f}%\")\n",
+    "print(\"-\" * 40)\n",
+    "\n",
+    "# Calculate loss\n",
+    "print(\"\\nCalculating loss...\")\n",
+    "loss_value = model.loss(X_sample, np.array([y_sample]))\n",
+    "print(f\"  Loss: {loss_value:.6f}\")\n",
+    "\n",
+    "# Calculate accuracy on training set\n",
+    "print(\"\\nQuick evaluation on full training set:\")\n",
+    "train_preds = model.predict(X_train)\n",
+    "train_acc = np.mean(train_preds == y_train)\n",
+    "print(f\"  Training accuracy (random init): {train_acc:.2%}\")\n",
+    "\n",
+    "print(\"\\n\" + \"=\" * 70)\n",
+    "print(\"Model test complete. The model is ready for training.\")\n",
+    "print(\"=\" * 70)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 42,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "np.float64(2.3088219587430108)"
+      ]
+     },
+     "execution_count": 42,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "model.loss(X_train, y_train)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 43,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "np.float64(0.0733464309102816)"
+      ]
+     },
+     "execution_count": 43,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "model.accuracy(X_train, y_train)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 44,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 1000x400 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "plot_prediction(model, sample_idx=5)"
    ]
@@ -711,9 +1211,32 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 45,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Random init: train loss: 2.30882, train acc: 0.073, test acc: 0.074\n",
+      "Epoch #1, train loss: 1.78161, train acc: 0.434, test acc: 0.400\n",
+      "Epoch #2, train loss: 1.28012, train acc: 0.712, test acc: 0.681\n",
+      "Epoch #3, train loss: 0.91842, train acc: 0.836, test acc: 0.837\n",
+      "Epoch #4, train loss: 0.69855, train acc: 0.870, test acc: 0.859\n",
+      "Epoch #5, train loss: 0.56301, train acc: 0.891, test acc: 0.867\n",
+      "Epoch #6, train loss: 0.47102, train acc: 0.910, test acc: 0.889\n",
+      "Epoch #7, train loss: 0.40062, train acc: 0.925, test acc: 0.904\n",
+      "Epoch #8, train loss: 0.34744, train acc: 0.935, test acc: 0.919\n",
+      "Epoch #9, train loss: 0.30832, train acc: 0.947, test acc: 0.926\n",
+      "Epoch #10, train loss: 0.27892, train acc: 0.957, test acc: 0.937\n",
+      "Epoch #11, train loss: 0.25594, train acc: 0.959, test acc: 0.941\n",
+      "Epoch #12, train loss: 0.23757, train acc: 0.961, test acc: 0.944\n",
+      "Epoch #13, train loss: 0.22275, train acc: 0.962, test acc: 0.944\n",
+      "Epoch #14, train loss: 0.21069, train acc: 0.965, test acc: 0.944\n",
+      "Epoch #15, train loss: 0.20070, train acc: 0.965, test acc: 0.941\n"
+     ]
+    }
+   ],
    "source": [
     "losses, accuracies, accuracies_test = [], [], []\n",
     "losses.append(model.loss(X_train, y_train))\n",
@@ -736,9 +1259,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 46,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAiMAAAGzCAYAAAD9pBdvAAAAOnRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjEwLjgsIGh0dHBzOi8vbWF0cGxvdGxpYi5vcmcvwVt1zgAAAAlwSFlzAAAPYQAAD2EBqD+naQAAPWJJREFUeJzt3Xl4lOWh/vF7JpPMZJskQPaEHSObAVmURUFFkSKKFavWBa32Zy1WKNZa27rU1nKsa13q0kWOtR61WsBdkVUUZA1bZYmsScgCIZns27y/PyYZCGsSkryzfD/XNRfJO9s95Tjc53ne93kshmEYAgAAMInV7AAAACC4UUYAAICpKCMAAMBUlBEAAGAqyggAADAVZQQAAJiKMgIAAExFGQEAAKaijAAAAFNRRgCc1K233qqePXu26bmPPPKILBZL+wZqoTPJDaDzUUYAP2SxWFp0W7p0qdlRAeC0LOxNA/ifN954o9nvr7/+uhYuXKh//vOfzY5feumlSkxMbPP71NXVye12y263t/q59fX1qq+vl8PhaPP7t9Wtt96qpUuXas+ePZ3+3gBaz2Z2AACtd9NNNzX7fdWqVVq4cOFxx49VWVmpiIiIFr9PaGhom/JJks1mk83GVwyA02OaBghQ48eP16BBg7Ru3TpdeOGFioiI0K9//WtJ0oIFCzR58mSlpKTIbrerT58++v3vf6+GhoZmr3HsuRd79uyRxWLRk08+qVdffVV9+vSR3W7XiBEjtGbNmmbPPdE5IxaLRXfffbfmz5+vQYMGyW63a+DAgfr000+Py7906VINHz5cDodDffr00SuvvHJG56FUVFTo3nvvVXp6uux2uzIyMvTkk0/q2MHhhQsXauzYsYqNjVVUVJQyMjK8/7s1ef755zVw4EBFREQoLi5Ow4cP15tvvtmmXAAYGQEC2qFDhzRp0iRdf/31uummm7xTNnPnzlVUVJRmz56tqKgoLV68WA899JBcLpeeeOKJ077um2++qbKyMt15552yWCz605/+pO9///vatWvXaUdTVqxYof/85z/66U9/qujoaD333HO65pprtG/fPnXt2lWStGHDBl1++eVKTk7W7373OzU0NOjRRx9VfHx8m/53MAxDV155pZYsWaLbb79dQ4YM0Weffab77rtPubm5euaZZyRJW7du1RVXXKFzzjlHjz76qOx2u7Kzs/XVV195X+uvf/2r7rnnHk2bNk0zZ85UdXW1Nm3apG+++UY//OEP25QPCHoGAL83Y8YM49j/nMeNG2dIMl5++eXjHl9ZWXncsTvvvNOIiIgwqqurvcemT59u9OjRw/v77t27DUlG165djeLiYu/xBQsWGJKMDz74wHvs4YcfPi6TJCMsLMzIzs72Htu4caMhyXj++ee9x6ZMmWJEREQYubm53mM7d+40bDbbca95Isfmnj9/viHJ+MMf/tDscdOmTTMsFos3zzPPPGNIMoqKik762ldddZUxcODA02YA0HJM0wABzG6367bbbjvueHh4uPfnsrIyHTx4UBdccIEqKyu1bdu2077uddddp7i4OO/vF1xwgSRp165dp33uhAkT1KdPH+/v55xzjpxOp/e5DQ0N+uKLLzR16lSlpKR4H9e3b19NmjTptK9/Ih9//LFCQkJ0zz33NDt+7733yjAMffLJJ5Kk2NhYSZ5pLLfbfcLXio2NVU5OznHTUgDajjICBLDU1FSFhYUdd3zr1q26+uqrFRMTI6fTqfj4eO/Jr6Wlpad93e7duzf7vamYHD58uNXPbXp+03MLCwtVVVWlvn37Hve4Ex1rib179yolJUXR0dHNjvfv3997v+QpWWPGjNEdd9yhxMREXX/99XrnnXeaFZP7779fUVFRGjlypPr166cZM2Y0m8YB0HqUESCAHT0C0qSkpETjxo3Txo0b9eijj+qDDz7QwoUL9fjjj0vSSUcEjhYSEnLC40YLVgo4k+d2tPDwcC1fvlxffPGFbr75Zm3atEnXXXedLr30Uu/Jvf3799f27dv11ltvaezYsXrvvfc0duxYPfzwwyanB/wXZQQIMkuXLtWhQ4c0d+5czZw5U1dccYUmTJjQbNrFTAkJCXI4HMrOzj7uvhMda4kePXooLy9PZWVlzY43TUn16NHDe8xqteqSSy7R008/rf/+97967LHHtHjxYi1ZssT7mMjISF133XV67bXXtG/fPk2ePFmPPfaYqqur25QPCHaUESDINI1MHD0SUVtbq7/85S9mRWomJCREEyZM0Pz585WXl+c9np2d7T23o7W+973vqaGhQS+88EKz488884wsFov3XJTi4uLjnjtkyBBJUk1NjSTPFUpHCwsL04ABA2QYhurq6tqUDwh2XNoLBJnRo0crLi5O06dP1z333COLxaJ//vOfPjFN0uSRRx7R559/rjFjxuiuu+7yFolBgwYpKyur1a83ZcoUXXTRRfrNb36jPXv2KDMzU59//rkWLFigWbNmeU+offTRR7V8+XJNnjxZPXr0UGFhof7yl78oLS1NY8eOlSRddtllSkpK0pgxY5SYmKhvv/1WL7zwgiZPnnzcOSkAWoYyAgSZrl276sMPP9S9996r3/72t4qLi9NNN92kSy65RBMnTjQ7niRp2LBh+uSTT/SLX/xCDz74oNLT0/Xoo4/q22+/bdHVPseyWq16//339dBDD+ntt9/Wa6+9pp49e+qJJ57Qvffe633clVdeqT179ugf//iHDh48qG7dumncuHH63e9+p5iYGEnSnXfeqX/96196+umnVV5errS0NN1zzz367W9/226fHwg27E0DwG9MnTpVW7du1c6dO82OAqAdcc4IAJ9UVVXV7PedO3fq448/1vjx480JBKDDMDICwCclJyfr1ltvVe/evbV371699NJLqqmp0YYNG9SvXz+z4wFoR5wzAsAnXX755fq///s/5efny263a9SoUfrjH/9IEQECECMjAADAVJwzAgAATEUZAQAApvKLc0bcbrfy8vIUHR0ti8VidhwAANAChmGorKxMKSkpslpPPv7hF2UkLy9P6enpZscAAABtsH//fqWlpZ30fr8oI01LLO/fv19Op9PkNAAAoCVcLpfS09NPu1WCX5SRpqkZp9NJGQEAwM+c7hQLTmAFAACmoowAAABTUUYAAICpKCMAAMBUlBEAAGAqyggAADAVZQQAAJiKMgIAAExFGQEAAKaijAAAAFNRRgAAgKkoIwAAwFRBW0bcbkPzN+Tq9rlr5KquMzsOAABBK2jLiMUivbgkW4u2FerTzflmxwEAIGgFcRmxaOrQVEnS/Kxck9MAABC8graMSNJVQ1IkSSt3HdKB0iqT0wAAEJyCuoykxUVoZM8uMgzp/aw8s+MAABCUgrqMSNJVQz2jI/MpIwAAmCLoy8jkwckKDbHo2wMubc8vMzsOAABBJ+jLSGxEmC7KSJDEiawAAJgh6MuIJO9VNQs25MrtNkxOAwBAcKGMSLr47ARF223KK63Wmj3FZscBACCoUEYkOUJDNGlwkiSmagAA6GyUkUZNUzUfbTqgmvoGk9MAABA8KCONzu/VVUlOh1zV9VqyrcjsOAAABA3KSCOr1eJdkXX+BqZqAADoLJSRozRN1SzeVqjSKnbyBQCgM1BGjtI/2amMxGjVNrj1yeYDZscBACAoUEaOwU6+AAB0LsrIMa5sPG9k1a5i5Zawky8AAB2NMnKM1NhwnderiyR28gUAoDNQRk7Auzw8UzUAAHQ4ysgJfG9QssJCrNqWX6ZvD7jMjgMAQECjjJxATESoLj6bnXwBAOgMlJGTmDrUcyLr+1l57OQLAEAHooycxPiMBDkdNh0ordY3u9nJFwCAjkIZOQlHaIi+NzhZEsvDAwDQkSgjp9B0Vc3Hmw+ouo6dfAEA6AiUkVMY2bOLUmIcKqup15JthWbHAQAgIFFGTsFqtejKIZ7RkXlM1QAA0CEoI6dxdeNUzdLtRSqprDU5DQAAgYcychoZSdE6O8mzk+/Hm/PNjgMAQMChjLTA1ezkCwBAh6GMtMCVQ1JksUirdxcr53Cl2XEAAAgolJEWSI4J1/m9ukqSFrCTLwAA7Yoy0kLeqZoNuTIMlocHAKC9UEZa6PLBSQqzWbWzsFz/ZSdfAADaDWWkhZyOUE3o79nJl6kaAADaD2WkFa5qXABtQVauGtjJFwCAdkEZaYXxGfGKCQ9VgatG3+w6ZHYcAAACAmWkFey2Izv5sjw8AADtgzLSSk1X1XyyJZ+dfAEAaAeUkVYa3iNOqbHhKq+p16Jv2ckXAIAzRRlpJavVoquGpEhiqgYAgPZAGWmDpqmaZTsKdbiCnXwBADgTlJE26JcYrQHJTtU1GPpo8wGz4wAA4NcoI23UNDqygJ18AQA4I5SRNmrayXfNnsPaX8xOvgAAtBVlpI0SnQ6N7tO0ky+jIwAAtFWrysicOXM0YsQIRUdHKyEhQVOnTtX27dtP+7x///vfOvvss+VwODR48GB9/PHHbQ7sS6Y2Lg8/j518AQBos1aVkWXLlmnGjBlatWqVFi5cqLq6Ol122WWqqKg46XO+/vpr3XDDDbr99tu1YcMGTZ06VVOnTtWWLVvOOLzZLh+UJLvNqu+KKrQ1j518AQBoC4txBv8vfVFRkRISErRs2TJdeOGFJ3zMddddp4qKCn344YfeY+eff76GDBmil19+uUXv43K5FBMTo9LSUjmdzrbG7RAz3lyvjzYd0B1je+m3VwwwOw4AAD6jpf9+n9E5I6WlpZKkLl26nPQxK1eu1IQJE5odmzhxolauXHnS59TU1MjlcjW7+aqmqZr3N+axky8AAG3Q5jLidrs1a9YsjRkzRoMGDTrp4/Lz85WYmNjsWGJiovLz80/6nDlz5igmJsZ7S09Pb2vMDjfurHjFRoSqsKxGK79jJ18AAFqrzWVkxowZ2rJli9566632zCNJeuCBB1RaWuq97d+/v93fo72E2ay64hx28gUAoK3aVEbuvvtuffjhh1qyZInS0tJO+dikpCQVFBQ0O1ZQUKCkpKSTPsdut8vpdDa7+bKmqZpPtxxQVS07+QIA0BqtKiOGYejuu+/WvHnztHjxYvXq1eu0zxk1apQWLVrU7NjChQs1atSo1iX1YcN6xCktLlwVtQ364tuC0z8BAAB4taqMzJgxQ2+88YbefPNNRUdHKz8/X/n5+aqqqvI+5pZbbtEDDzzg/X3mzJn69NNP9dRTT2nbtm165JFHtHbtWt19993t9ylMZrFYvKMj85mqAQCgVVpVRl566SWVlpZq/PjxSk5O9t7efvtt72P27dunAweObB43evRovfnmm3r11VeVmZmpd999V/Pnzz/lSa/+aOrQFEnSsh1FKmYnXwAAWuyM1hnpLL68zsjRrnj+S23Jden3Vw3UzaN6mh0HAABTdco6I2jOO1WTlWdyEgAA/AdlpB1dmZkiq0Vat/ew9h1iJ18AAFqCMtKOEpwOjenbTZI0n518AQBoEcpIOzsyVcNOvgAAtARlpJ1NHJQkR6hVu4oqtDm31Ow4AAD4PMpIO4uy23TpAM/qsvM3cCIrAACnQxnpAFc3rjny/sY81Te4TU4DAIBvo4x0gAv6xatLZJgOltfoa3byBQDglCgjHSA05MhOviwPDwDAqVFGOshVTTv5bs1XZW29yWkAAPBdlJEOcm73WHXvEqHK2gYt/C87+QIAcDKUkQ7i2cnXcyIrUzUAAJwcZaQDXTXUM1WzfOdBHSqvMTkNAAC+iTLSgfrERykzLUYNbkMfbjpgdhwAAHwSZaSDXXXU8vAAAOB4lJEONiUzRSFWizbsK9GegxVmxwEAwOdQRjpYfLSdnXwBADgFykgnaFoefkFWHjv5AgBwDMpIJ7hsQJLCQ0O0+2CFNuawky8AAEejjHSCSLtNlw1MlMSaIwAAHIsy0kmmNq458uEmdvIFAOBolJFOckHfbuoaGaaD5bVakX3Q7DgAAPgMykgnsYVYNSWT5eEBADgWZaQTXdW4V81nWwtUUcNOvgAASJSRTjUkPVY9u0aoqo6dfAEAaEIZ6UQWi8V7Ius8pmoAAJBEGel0Uxv3qlmRfVBFZezkCwAAZaST9ewWqSHpsWpwG3p/Y57ZcQAAMB1lxATXnOsZHXl3XY7JSQAAMB9lxARXZqYqLMSqbw+4tDWP5eEBAMGNMmKCmIhQXdq4PDyjIwCAYEcZMcm0YWmSPDv51tazPDwAIHhRRkxyQd9uSoi2q7iiVou3FZodBwAA01BGTGILser753pGR5iqAQAEM8qIiaYN81xVs2R7IWuOAACCFmXERH0Tor1rjizIYkVWAEBwooyYrOlE1n+vzZFhGCanAQCg81FGTDYlM0VhNqu2F5Rpa57L7DgAAHQ6yojJYsJDNXFgkiTp32v3m5wGAIDORxnxAd41Rzbmqaa+weQ0AAB0LsqIDxjbt5uSnA6VVNZp8besOQIACC6UER8QYrXo+2yeBwAIUpQRH3FN41TN0h1FKiyrNjkNAACdhzLiI/rER+nc7p41R+ZvYM0RAEDwoIz4kGuHp0vyTNWw5ggAIFhQRnzI5HOSZbdZtaOgXJtzS82OAwBAp6CM+BCnI1SXD2pac4QTWQEAwYEy4mOuHeaZqnl/Y56q61hzBAAQ+CgjPmZUn65KiXGotKpOi1hzBAAQBCgjPsaz5kjj5nnrWB4eABD4KCM+qGnNkeU7ilTgYs0RAEBgo4z4oF7dIjWiZ5zchjSPNUcAAAGOMuKjmjbPY80RAECgo4z4qO8NTpYj1KrswnJl7S8xOw4AAB2GMuKjoh2hmjQoWRKb5wEAAhtlxIdd2zhVw5ojAIBARhnxYef37qrU2HCVVdfr8/8WmB0HAIAOQRnxYVarRdecmyqJqRoAQOCijPi4pjVHVuwsUn4pa44AAAIPZcTH9egaqZG9ushtSP/ZwOgIACDwUEb8gHfNkbWsOQIACDyUET/wvcHJCg8N0a6DFVq/r8TsOAAAtCvKiB+Istv0vcGsOQIACEyUET/RNFXzIWuOAAACDGXET5zXq4vS4sJVVlOvz7bmmx0HAIB2QxnxE541R45sngcAQKCgjPiRpqmaFdkHlVdSZXIaAADaB2XEj6R3idD5vbvIMKT/rGd0BAAQGCgjfmbasHRJnqka1hwBAASCVpeR5cuXa8qUKUpJSZHFYtH8+fNP+filS5fKYrEcd8vP5yTMtvje4CRFhoVoz6FKrdt72Ow4AACcsVaXkYqKCmVmZurFF19s1fO2b9+uAwcOeG8JCQmtfWtIighjzREAQGCxtfYJkyZN0qRJk1r9RgkJCYqNjW3RY2tqalRTU+P93eVytfr9Atm0YWn697ocfbjpgB6aMkARYa3+awQAwGd02jkjQ4YMUXJysi699FJ99dVXp3zsnDlzFBMT472lp6d3Ukr/MLJXF3XvEqFy1hwBAASADi8jycnJevnll/Xee+/pvffeU3p6usaPH6/169ef9DkPPPCASktLvbf9+/d3dEy/YrFYjmyex1QNAMDPdfj4fkZGhjIyMry/jx49Wt99952eeeYZ/fOf/zzhc+x2u+x2e0dH82vfPzdVTy/coa+/O6Scw5VKi4swOxIAAG1iyqW9I0eOVHZ2thlvHTDS4iI0uk/XxjVHcs2OAwBAm5lSRrKyspScnGzGWweUo6dqWHMEAOCvWj1NU15e3mxUY/fu3crKylKXLl3UvXt3PfDAA8rNzdXrr78uSXr22WfVq1cvDRw4UNXV1frb3/6mxYsX6/PPP2+/TxGkLh+UpIcWbNW+4kqt2XNYI3t1MTsSAACt1uqRkbVr12ro0KEaOnSoJGn27NkaOnSoHnroIUnSgQMHtG/fPu/ja2trde+992rw4MEaN26cNm7cqC+++EKXXHJJO32E4BURZtPkxjVH/r2Wk3wBAP7JYvjB+L7L5VJMTIxKS0vldDrNjuNT1uwp1rUvr1REWIjW/GaCIu2sOQIA8A0t/febvWn83PAecerZNUKVtQ36dAtrjgAA/A9lxM+x5ggAwN9RRgLA1eemyWKRVu46pP3FlWbHAQCgVSgjASA1Nlxj+nSTJL23ntERAIB/oYwEiGuHe6Zq3lufI7fb589JBgDAizISIC4bkKRou037i6v0ze5is+MAANBilJEAER4WoisyPWuOcCIrAMCfUEYCyLRh6ZKkT7YcUEVNvclpAABoGcpIADm3e6x6d4tUZW2DPt58wOw4AAC0CGUkgFgsFl3TuObIv5mqAQD4CcpIgPn+uamyWqTVu4u191CF2XEAADgtykiASY4J19h+8ZKk99bnmpwGAIDTo4wEoKbl4d9bx5ojAADfRxkJQJcNSFS0w6bckiqt2nXI7DgAAJwSZSQAOUJDNCUzRRJrjgAAfB9lJEBd2zhV8/GWAyqrrjM5DQAAJ0cZCVBD0mPVJz5S1XVu1hwBAPg0ykiAslgs3hVZmaoBAPgyykgAa1pzZM2ew9pzkDVHAAC+iTISwBKdDl14VtOaI4yOAAB8E2UkwB295kgDa44AAHwQZSTATeifKKfDprzSaq38jjVHAAC+hzIS4ByhIbpqSKok6d11+01OAwDA8SgjQaBpquaTLflyseYIAMDHUEaCwDlpMeqXEKWaerc+2sSaIwAA30IZCQKeNUc8oyOsOQIA8DWUkSBx9dBUhVgtWrf3sHYVlZsdBwAAL8pIkEhwOjSucc2Rv63YbXIaAACOoIwEkZ+M6yNJemfNfu09xIqsAADfQBkJIiN7ddG4s+JV7zb07Bc7zY4DAIAkykjQ+cVlGZKk+Vm52lFQZnIaAAAoI0FncFqMJg1KkmFIT32+3ew4AABQRoLR7EvPktUifba1QBv3l5gdBwAQ5CgjQahfYrSmDvUsEf8koyMAAJNRRoLUzyecpdAQi77ceVCrdrGBHgDAPJSRIJXeJULXjUiXJD352XYZhmFyIgBAsKKMBLGfXdxPdptVa/ce1tIdRWbHAQAEKcpIEEt0OjR9dE9JntERt5vREQBA56OMBLmfjOujKLtNW/Nc+nRrvtlxAABBiDIS5LpEhun2sb0kedYdaWB0BADQySgj0B0X9FJsRKi+K6rQvA25ZscBAAQZyggU7QjVXY2b6D37xQ7V1rtNTgQACCaUEUiSbhnVUwnRduUcrtLba/aZHQcAEEQoI5AkhYeF6GcX95UkPbc4W1W1DSYnAgAEC8oIvK4b0V1pceEqKqvR6yv3mB0HABAkKCPwCrNZNWvCWZKkl5Z9J1d1ncmJAADBgDKCZq4emqq+CVEqqazT37/cbXYcAEAQoIygmRCrRbMv9YyO/O3LXSquqDU5EQAg0FFGcJzLByZpUKpTFbUNennZd2bHAQAEOMoIjmO1WnTvZRmSpP/9eo8KXNUmJwIABDLKCE5o/FnxGtEzTjX1bj2/eKfZcQAAAYwyghOyWCz6RePoyFur92vfoUqTEwEAAhVlBCd1Xu+uuvCseNW7DT27aIfZcQAAAYoyglP6xWWeK2vmbcjVzoIyk9MAAAIRZQSndE5arC4fmCTDkJ5eyOgIAKD9UUZwWrMvO0sWi/TJlnxtzik1Ow4AIMBQRnBaZyVG6+ohqZKkJz/fbnIaAECgoYygRWZNOEs2q0XLdhRp9e5is+MAAAIIZQQt0r1rhK4bkS5JeuKzbTIMw+REAIBAQRlBi/3s4n6y26xas+ewlu0oMjsOACBAUEbQYkkxDt0yqockz7kjjI4AANoDZQStctf4vooMC9GWXJc+3ZJvdhwAQACgjKBVukSG6fYLekuSnlq4Qw1uRkcAAGeGMoJWu+OCXooJD1V2Ybnmb8g1Ow4AwM9RRtBqTkeo7hrfR5L07KIdqq13m5wIAODPKCNok+mjeio+2q79xVV6e+1+s+MAAPwYZQRtEh4Wop9d3FeS9PyinaquazA5EQDAX1FG0GbXj+iu1NhwFZbV6PWVe8yOAwDwU60uI8uXL9eUKVOUkpIii8Wi+fPnn/Y5S5cu1bnnniu73a6+fftq7ty5bYgKXxNms2rWhH6SpJeWfqey6jqTEwEA/FGry0hFRYUyMzP14osvtujxu3fv1uTJk3XRRRcpKytLs2bN0h133KHPPvus1WHhe64emqre8ZE6XFmnv6/YbXYcAIAfshhnsIymxWLRvHnzNHXq1JM+5v7779dHH32kLVu2eI9df/31Kikp0aefftqi93G5XIqJiVFpaamcTmdb46KDfLTpgGa8uV5Rdpu+/OVFiosMMzsSAMAHtPTf7w4/Z2TlypWaMGFCs2MTJ07UypUrT/qcmpoauVyuZjf4rkmDkjQg2anymnq9vOw7s+MAAPxMh5eR/Px8JSYmNjuWmJgol8ulqqqqEz5nzpw5iomJ8d7S09M7OibOgNVq0X0TMyRJ/7tyjwpc1SYnAgD4E5+8muaBBx5QaWmp97Z/P+tY+LrxGfEa1iNO1XVuvbA42+w4AAA/0uFlJCkpSQUFBc2OFRQUyOl0Kjw8/ITPsdvtcjqdzW7wbRbLkdGRt9bs0/7iSpMTAQD8RYeXkVGjRmnRokXNji1cuFCjRo3q6LdGJzu/d1dd0K+b6hoMPfvFTrPjAAD8RKvLSHl5ubKyspSVlSXJc+luVlaW9u3bJ8kzxXLLLbd4H/+Tn/xEu3bt0i9/+Utt27ZNf/nLX/TOO+/o5z//eft8AviUX1zmGR2ZtyFH2YVlJqcBAPiDVpeRtWvXaujQoRo6dKgkafbs2Ro6dKgeeughSdKBAwe8xUSSevXqpY8++kgLFy5UZmamnnrqKf3tb3/TxIkT2+kjwJdkpsfqsgGJchvS0wt3mB0HAOAHzmidkc7COiP+ZXt+mS7/83IZhvThz8ZqUGqM2ZEAACbwmXVGEHwykqJ1VWaKJOnJz7ebnAYA4OsoI+gQsyacJZvVoqXbi7RmT7HZcQAAPowygg7Rs1ukrh3uWazuic+2yw9mAwEAJqGMoMPcc0lfhdmsWr27WF/uPGh2HACAj6KMoMMkx4Tr5vN7SPKcO8LoCADgRCgj6FA/Hd9HkWEh2pRTqnkbcs2OAwDwQZQRdKiuUXbdOa6PJOmB/2zWhn2HTU4EAPA1lBF0uBkX9dUlZyeopt6tH7++ln1rAADNUEbQ4UKsFj13w1ANTHHqYHmtfjR3jUqr6syOBQDwEZQRdIpIu01/nz5CSU6HdhaW66f/Wqe6BrfZsQAAPoAygk6TFOPQ328droiwEH2VfUi/nbeFK2wAAJQRdK6BKTF64YdDZbVIb6/dr5eX7TI7EgDAZJQRdLqLz07Uw1MGSpIe/3SbPtp0wOREAAAzUUZgiumje+q2MT0lSbPfydJ6LvkFgKBFGYFpfjt5gCb0b7zk93+55BcAghVlBKYJsVr05+s9l/weqqjVra+t5pJfAAhClBGY6uhLfr8rqtBdb6xTbT2X/AJAMKGMwHRJMQ7949YRigwL0dffHdJv52/mkl8ACCKUEfiEASlOvfDDc2W1SO+szdFLy74zOxIAoJNQRuAzLjo7QY9c6bnk90+fbteHm/JMTgQA6AyUEfiUW0b11I/G9JIkzX5no9bt5ZJfAAh0lBH4nN9M7q8J/RNVW+/W/3t9rfYd4pJfAAhklBH4HM8lv0M0KNVzye9tc1ertJJLfgEgUFFG4JOaLvlNjmm85PdfXPILAIGKMgKfleh06O/Tj1zy+5t5XPILAIGIMgKfNiDFqRdu9Fzy++91OfrLUi75BYBAQxmBz7soI0G/a7zk94nPtuuDjVzyCwCBhDICv3DzqJ66faznkt97/71R6/YWm5wIANBeKCPwG7/+3pFLfn/8+jou+QWAAEEZgd8IsVr03A2eS36LK2p1K5f8AkBAoIzAr0SEHbnkd1dRhX7CLr8A4PcoI/A7ic4ju/yu3HVIv+aSXwDwa5QR+KX+yUcu+X13XY5eXJJtdiQAQBtRRuC3LspI0O+uGiRJevLzHXqfS34BwC9RRuDXbj6/h+5ovOT3F1zyCwB+iTICv/fA9/rr0gFHLvnde6jC7EgAgFagjMDvNe3yOzg1RsUVtbpt7hqVVNaaHQsA0EKUEQQEzyW/w5XCJb8A4HcoIwgYCU6H/n7rCEXZbVq1q1gP/IdLfgHAH1BGEFD6Jzv1wg+HKsRq0Xvrc/TCYi75BQBfRxlBwBmfkaBHGnf5fWrhDr26/Du53YyQAICvoowgIN18fg/9vwt7S5L++PE23Tp3jQrLqk1OBQA4EcoIAtYDk87WY1cPkiPUquU7ijTp2S+1eFuB2bEAAMegjCBgWSwW3XheD31w91j1T3bqUEWtfjR3rR55f6uq6xrMjgcAaEQZQcDrlxiteT8drR+N8azUOvfrPZr64lfaUVBmcjIAgEQZQZBwhIbooSkD9NptI9QtKkzb8ss05fkV+ufKPVz+CwAmo4wgqFyUkaBPZl6ocWfFq6berQcXbNWPX1+n4gpWbAUAs1BGEHTio+167dYReuiKAQoLseqLbwt0+bPLtWLnQbOjAUBQoowgKFmtFv1obC/NnzFGfROiVFhWo5v+/o3mfPwty8gDQCejjCCoDUhx6oO7x+rG87pLkl5ZvkvXvPS1dhWVm5wMAIIHZQRBLzwsRI9dPViv3DxMsRGh2pxbqsnPrdA7a/ZzcisAdALKCNBo4sAkfTrzQo3q3VVVdQ365XubdPebG1RaWWd2NAAIaJQR4ChJMQ69ccd5uv/ys2WzWvTR5gOa9OflWr272OxoABCwKCPAMUKsFt01vo/eu2u0enaNUF5pta5/daWe/ny76hs4uRUA2htlBDiJzPRYfXjPBZo2LE1uQ3pucbZ+8MpK7S+uNDsaAAQUyghwClF2m568NlPP3TBU0Xab1u8r0aQ/f6kFWblmRwOAgEEZAVrgyswUfTzzAg3vEafymnrNfCtLs9/OUlk1J7cCwJmijAAtlN4lQm/9v/M1a0I/WS3SfzbkavJzK7Rh32GzowGAX6OMAK1gC7Fq1oSz9M6do5QaG659xZWa9vJKvbB4pxrcrEkCAG1BGQHaYHjPLvp45gW64pxkNbgNPfn5Dt3w11XKK6kyOxoA+B3KCNBGMeGhev6GoXry2kxFhIVo9e5iTfrzl/pk8wGzowGAX6GMAGfAYrFo2rA0fXTPBTonLUalVXW661/r9av3Nqm8pt7seADgFygjQDvo1S1S7/5ktO4a30cWi/TWmv0a8z+L9ecvdqq0iituAOBULIYf7ATmcrkUExOj0tJSOZ1Os+MAp/R19kH9dv4W7TpYIUmKtts0fXRP3T62l+Iiw0xOBwCdp6X/flNGgA7Q4Db00eYDemHxTu0oKJckRYSF6Obze+iOC3orPtpuckIA6HiUEcAHuN2GPv9vvp5fnK2teS5JkiPUqhtGdtedF/ZRUozD5IQA0HEoI4APMQxDS7YX6rlF2craXyJJCgux6trhabprfB+lxUWYGxAAOgBlBPBBhmFoRfZBPb8oW6v3FEuSbFaLvn9uqn46vq96dos0OSEAtB/KCODjVu06pOcX79RX2YckSVaLdNWQVM24qI/6JkSbnA4AzlxL//1u06W9L774onr27CmHw6HzzjtPq1evPulj586dK4vF0uzmcDBPDpzfu6v+dcf5eu+u0booI15uQ5q3IVeXPrNcM/61Xt8ecJkdEQA6RavLyNtvv63Zs2fr4Ycf1vr165WZmamJEyeqsLDwpM9xOp06cOCA97Z3794zCg0EkmE94vTabSP1wd1jddmARBmG9NHmA5r05y/149fXanNOqdkRAaBDtXqa5rzzztOIESP0wgsvSJLcbrfS09P1s5/9TL/61a+Oe/zcuXM1a9YslZSUtDkk0zQIJt8ecOmFJdn6ePMBNf3XOT4jXj+7uJ+G9YgzNxwAtEKHTNPU1tZq3bp1mjBhwpEXsFo1YcIErVy58qTPKy8vV48ePZSenq6rrrpKW7duPeX71NTUyOVyNbsBwaJ/slMv/vBcLfz5hbp6aKqsFmnp9iJd89LXuvFvq7Rq1yGzIwJAu2pVGTl48KAaGhqUmJjY7HhiYqLy8/NP+JyMjAz94x//0IIFC/TGG2/I7XZr9OjRysnJOen7zJkzRzExMd5benp6a2ICAaFvQrSeuW6IFt87XtcNT5fNatFX2Yd0/aur9IOXV2r5jiL5wfnnAHBarZqmycvLU2pqqr7++muNGjXKe/yXv/ylli1bpm+++ea0r1FXV6f+/fvrhhtu0O9///sTPqampkY1NTXe310ul9LT05mmQVDLOVypl5d9p3fW5Ki2wS1JGpIeq59d3FcXn50gi8VickIAaK5Dpmm6deumkJAQFRQUNDteUFCgpKSkFr1GaGiohg4dquzs7JM+xm63y+l0NrsBwS4tLkJ/mDpYy395kW4b01N2m1VZ+0t0+/+u1RXPr9CnWw7I7WakBID/aVUZCQsL07Bhw7Ro0SLvMbfbrUWLFjUbKTmVhoYGbd68WcnJya1LCkCSlBTj0MNTBmrF/RfrznG9FREWoq15Lv3kjfW6/M/L9eY3+9gpGIBfafXVNG+//bamT5+uV155RSNHjtSzzz6rd955R9u2bVNiYqJuueUWpaamas6cOZKkRx99VOeff7769u2rkpISPfHEE5o/f77WrVunAQMGtOg9uZoGOLnDFbX6x1e7NferPSqrqZck2W1WXTYwSdOGpWls324KsTKFA6DztfTfb1trX/i6665TUVGRHnroIeXn52vIkCH69NNPvSe17tu3T1brkQGXw4cP68c//rHy8/MVFxenYcOG6euvv25xEQFwanGRYbr3sgzdcUFvvb1mn95dl6MdBeX6YGOePtiYp0SnXVcPTdO0Yams7ArAJ7EcPBBgDMPQ5txSvbcuRws25qmk8siUTWZ6rKadm6opmSmKjQgzMSWAYMDeNABUU9+gJdsK9e66HC3ZXqSGxhNcw0KsunRAoq4ZlqoL+8XLFtKmnSEA4JQoIwCaKSqr0YKsXL27Lkfb8su8x+Oj7Zo6JEXThqUrI4lpHADthzIC4KS25pXq3XU5WpCVp+KKWu/xwakxuubcVF05JFVdIpnGAXBmKCMATqu23q2l2wv13vocLfq2UPWN0zihIRZdfHaCpg1L1/iMeIUyjQOgDSgjAFqluKJW72fl6t31OdqSe2Q/qK6RYbpqSKquGZaqgSkxJiYE4G8oIwDabFu+S++ty9G8DXk6WH5ka4b+yU5dc26qpg5NVbcou4kJAfgDygiAM1bf4NbynUV6d12OvvhvoXdPHJvVovEZ8Zo2LE0Xn52oMBvTOACORxkB0K5KKmv1wcY8vbsuRxtzSr3HYyNCdVVmiiYNTtawHnGcXwLAizICoMPsLCjTu+tzNG99rgrLjkzjRNltGtO3q8adlaBxGfFKjQ03MSUAs1FGAHS4+ga3VmQf1IKsPC3bUdTsMmFJ6pcQpXFnxWtcRrxG9OwiR2iISUkBmIEyAqBTud2eZeiX7SjSsh1F2rDvsNxHfbs4Qq0a1burxmckaNxZ8erZLdK8sAA6BWUEgKlKK+u0Ivuglu0o1LIdRSpw1TS7v0fXCM+oyVnxGtWnqyLCWr1vJwAfRxkB4DMMw9C2/DLPqMn2Iq3dW6y6hiNfPWEhVo3s1cU7pdMvIUoWi8XExADaA2UEgM8qr6nX19kHtWxHkZZuL1JuSVWz+1NiHBqX4Rk1Gd23m5yOUJOSAjgTlBEAfsEwDO06WKFl2z3nmqzadUg19W7v/SFWi4Z1j/OWkwHJTlmtjJoA/oAyAsAvVdc1aNWuQ94TYXcVVTS7v1uUXRee1U3jzorXBf3i2dAP8GGUEQABYX9xpXc65+vvDqqytsF7n8UiZSRG65y0GJ2TFqvMtFhlJEWzIizgIygjAAJObb1ba/cWe0+E3ZZfdtxjwmxW9U92KtNbUGLUOz5KIUztAJ2OMgIg4BW6qpW1v0Sbckq1McfzZ2lV3XGPiwwL0aDUGGWmx+qctBhlpsUqLS6cK3aADkYZARB0DMPQvuJKbcwp1ab9JdqYU6ItuS5V1TUc99gukWFHTe94/oyPZidioD1RRgBAniXrs4vKtWn/kdGTbfmuZuucNEmJceictFidk+4ZPRmcFsNlxcAZoIwAwElU1zVoW36ZNuWUaOP+Um3KKVF2UblO9G3Yu1vkkRGU9BgNTIlhjx2ghSgjANAK5TX12pJb6i0oG3NKlHO46rjHhVgtykiM1tnJ0eoTH6U+8VHqmxCp7l0iuYoHOAZlBADO0KHyGm3KLdWmxtGTjTmlOlhec8LHhlgt6tElQr3jo9QnIdJbVPrERyo2grVQEJwoIwDQzgzD0IHSam3KKVV2YZm+K6rQd0Xl+q6wXBW1x58k26RbVJh6dzu2pEQpNS6cS44R0Fr67zfbZAJAC1ksFqXEhislNlxSkve4YRgqcNV4ikljOWkqKgdKq3WwvFYHy4u1ek9xs9cLs1nVu1ukdwSlT4KnpPTqFqlIO1/PCB6MjABAByqvqdfuphGUonLtavx518EK1R61B8+xkmMcx5WUPvFRSnTaWR8FfoNpGgDwYQ1uQ7mHq46MphSV67tCT1E5VFF70ufZbValxoYrNS5caXHh3p9TYyOUFheuRKeDqR/4DMoIAPipwxW12nXwSDlpGlHZW1ypBvepv7JtVouSYhxHFZYIpXkLS7iSYx2y27g0GZ2DMgIAAaa23q0DpVXKPVylnJLGPw9XKbekUrklVTpQUq3605QVi0VKiLY3lpWIZqMsTaUlIozzVdA+OIEVAAJMmM2qHl0j1aNr5Anvb3AbKiyr9hSUw1XKLWkqK1XKPewpLNV1bhW4alTgqtH6fSUnfJ24iFBPQYmNODKiEuNQgtOuhGiH4qPtLPyGdkUZAYAAEWK1KDkmXMkx4RrR8/j7DcPQoYpab1E5UlgqvaWlrLpehyvrdLiyTltyXSd9L6fDpgSnQwnRds+t8ef4aE9h8RQXu6LsNk64xWlRRgAgSFgsFnWLsqtblF2Z6bEnfIyrus5TUg57Skpuiaek5JdWq7CsRoVlNaqtd8tVXS9XdbmyC8tP+Z7hoSGNBcXebGTl6AKTEG1XXESYrJx4G7QoIwAAL6cjVM7kUPVPPvH8vmEYclXVq7CsqZxUq6isRoWuGu/vhWU1KnLVqKymXlV1DdpXXKl9xZWnfF+b1eItKfGNIyvxUZ6Rli6RYYqLCPP8GRmquIgwhYaw9H4goYwAAFrMYrEoJiJUMRGh6pcYfcrHVtU2HCktrprjfi4qq1FRWY0OVdSq3u1Z3fZAabWk0tPmcDpsjeUkTF0iGv9sLC1dm443FpcukWFyOkIZefFhlBEAQIcIDws55Qm3Teoa3DpYfszoSuPPh8prdLiyVsUVtY3nstTKMNQ4TVSvPYdOPeLSxGrRUaMrRxeY0GbHuzYWmtiIUM536USUEQCAqUJDrN4Tb0+nwW3IVVWnQxW1R0pKRa2KKxv/rKhTcUWNiivrdLjxvrKaerkN6VBF7SkXlDuW1SI5w0PldIQqJjxUznDbUT+HyumwHfnZ+zib92euOGo5yggAwG+EWC2KaxzFaKnaerdKKj2Fpbi8eXE5MupSq0PljX9W1Kq23i23IZVU1qmksq5NWcNsVk9ZcXgKSkxjSXGG2476uflxpyNUkXaboh022W3WoBmZoYwAAAJamM3quXLH6WjR4w3DUE29W6VVdXJV1clVXdf4c73n50rPMVdVved40/3VRx5jGJ4S1HReTFuEWC2KstuO3Bw2T1Gx2xRpD1GUPVRRDpuiGn+OtIco2mFTZJjnsdGNx6IcNp9fdZcyAgDAUSwWixyhIXKEhiixhQXmaG63ofLaermqjikx3nLjuc97/1HFpqy6ThW1DZI8U1KljY85U6EhnmIT2Vhsoh1Hfm663Tyqx2nP7+kolBEAANqR1WrxTLs4QpUW1/rnu92GKmrrVVHToPKaOpVVH/m5vKZB5Y2Fpay6XuU1daqoaWh8TL3Kazx/ltXUq7zac2m1JNU1GN7F7E5m8jnJlBEAAOApM9GOUEU7QiW1fmTmaPUNblXUNniLSnljSTm6sDTdlxJ7+hOIOwplBACAAGULsSom3HMirS9jCTsAAGAqyggAADAVZQQAAJiKMgIAAExFGQEAAKaijAAAAFNRRgAAgKkoIwAAwFSUEQAAYCrKCAAAMBVlBAAAmIoyAgAATEUZAQAApvKLXXsNw5AkuVwuk5MAAICWavp3u+nf8ZPxizJSVlYmSUpPTzc5CQAAaK2ysjLFxMSc9H6Lcbq64gPcbrfy8vIUHR0ti8XSbq/rcrmUnp6u/fv3y+l0ttvr+qpg+7xS8H1mPm9g4/MGtkD8vIZhqKysTCkpKbJaT35miF+MjFitVqWlpXXY6zudzoD5i2+JYPu8UvB9Zj5vYOPzBrZA+7ynGhFpwgmsAADAVJQRAABgqqAuI3a7XQ8//LDsdrvZUTpFsH1eKfg+M583sPF5A1uwfd6j+cUJrAAAIHAF9cgIAAAwH2UEAACYijICAABMRRkBAACmoowAAABTBXUZefHFF9WzZ085HA6dd955Wr16tdmROsScOXM0YsQIRUdHKyEhQVOnTtX27dvNjtVp/ud//kcWi0WzZs0yO0qHyc3N1U033aSuXbsqPDxcgwcP1tq1a82O1SEaGhr04IMPqlevXgoPD1efPn30+9///rQbcfmT5cuXa8qUKUpJSZHFYtH8+fOb3W8Yhh566CElJycrPDxcEyZM0M6dO80J2w5O9Xnr6up0//33a/DgwYqMjFRKSopuueUW5eXlmRf4DJ3u7/doP/nJT2SxWPTss892Wj4zBG0ZefvttzV79mw9/PDDWr9+vTIzMzVx4kQVFhaaHa3dLVu2TDNmzNCqVau0cOFC1dXV6bLLLlNFRYXZ0TrcmjVr9Morr+icc84xO0qHOXz4sMaMGaPQ0FB98skn+u9//6unnnpKcXFxZkfrEI8//rheeuklvfDCC/r222/1+OOP609/+pOef/55s6O1m4qKCmVmZurFF1884f1/+tOf9Nxzz+nll1/WN998o8jISE2cOFHV1dWdnLR9nOrzVlZWav369XrwwQe1fv16/ec//9H27dt15ZVXmpC0fZzu77fJvHnztGrVKqWkpHRSMhMZQWrkyJHGjBkzvL83NDQYKSkpxpw5c0xM1TkKCwsNScayZcvMjtKhysrKjH79+hkLFy40xo0bZ8ycOdPsSB3i/vvvN8aOHWt2jE4zefJk40c/+lGzY9///veNG2+80aREHUuSMW/ePO/vbrfbSEpKMp544gnvsZKSEsNutxv/93//Z0LC9nXs5z2R1atXG5KMvXv3dk6oDnSyz5uTk2OkpqYaW7ZsMXr06GE888wznZ6tMwXlyEhtba3WrVunCRMmeI9ZrVZNmDBBK1euNDFZ5ygtLZUkdenSxeQkHWvGjBmaPHlys7/nQPT+++9r+PDhuvbaa5WQkKChQ4fqr3/9q9mxOszo0aO1aNEi7dixQ5K0ceNGrVixQpMmTTI5WefYvXu38vPzm/3fdUxMjM4777yg+P6SPN9hFotFsbGxZkfpEG63WzfffLPuu+8+DRw40Ow4ncIvdu1tbwcPHlRDQ4MSExObHU9MTNS2bdtMStU53G63Zs2apTFjxmjQoEFmx+kwb731ltavX681a9aYHaXD7dq1Sy+99JJmz56tX//611qzZo3uuecehYWFafr06WbHa3e/+tWv5HK5dPbZZyskJEQNDQ167LHHdOONN5odrVPk5+dL0gm/v5ruC2TV1dW6//77dcMNNwTUzrZHe/zxx2Wz2XTPPfeYHaXTBGUZCWYzZszQli1btGLFCrOjdJj9+/dr5syZWrhwoRwOh9lxOpzb7dbw4cP1xz/+UZI0dOhQbdmyRS+//HJAlpF33nlH//rXv/Tmm29q4MCBysrK0qxZs5SSkhKQnxdH1NXV6Qc/+IEMw9BLL71kdpwOsW7dOv35z3/W+vXrZbFYzI7TaYJymqZbt24KCQlRQUFBs+MFBQVKSkoyKVXHu/vuu/Xhhx9qyZIlSktLMztOh1m3bp0KCwt17rnnymazyWazadmyZXruuedks9nU0NBgdsR2lZycrAEDBjQ71r9/f+3bt8+kRB3rvvvu069+9Stdf/31Gjx4sG6++Wb9/Oc/15w5c8yO1imavqOC7furqYjs3btXCxcuDNhRkS+//FKFhYXq3r279/tr7969uvfee9WzZ0+z43WYoCwjYWFhGjZsmBYtWuQ95na7tWjRIo0aNcrEZB3DMAzdfffdmjdvnhYvXqxevXqZHalDXXLJJdq8ebOysrK8t+HDh+vGG29UVlaWQkJCzI7YrsaMGXPcpdo7duxQjx49TErUsSorK2W1Nv/qCgkJkdvtNilR5+rVq5eSkpKafX+5XC598803Afn9JR0pIjt37tQXX3yhrl27mh2pw9x8883atGlTs++vlJQU3Xffffrss8/MjtdhgnaaZvbs2Zo+fbqGDx+ukSNH6tlnn1VFRYVuu+02s6O1uxkzZujNN9/UggULFB0d7Z1XjomJUXh4uMnp2l90dPRx58NERkaqa9euAXmezM9//nONHj1af/zjH/WDH/xAq1ev1quvvqpXX33V7GgdYsqUKXrsscfUvXt3DRw4UBs2bNDTTz+tH/3oR2ZHazfl5eXKzs72/r57925lZWWpS5cu6t69u2bNmqU//OEP6tevn3r16qUHH3xQKSkpmjp1qnmhz8CpPm9ycrKmTZum9evX68MPP1RDQ4P3O6xLly4KCwszK3abne7v99iyFRoaqqSkJGVkZHR21M5j9uU8Znr++eeN7t27G2FhYcbIkSONVatWmR2pQ0g64e21114zO1qnCeRLew3DMD744ANj0KBBht1uN84++2zj1VdfNTtSh3G5XMbMmTON7t27Gw6Hw+jdu7fxm9/8xqipqTE7WrtZsmTJCf+bnT59umEYnst7H3zwQSMxMdGw2+3GJZdcYmzfvt3c0GfgVJ939+7dJ/0OW7JkidnR2+R0f7/HCoZLey2GEUDLFgIAAL8TlOeMAAAA30EZAQAApqKMAAAAU1FGAACAqSgjAADAVJQRAABgKsoIAAAwFWUEAACYijICAABMRRkBAACmoowAAABT/X9oWHtJdyjHXAAAAABJRU5ErkJggg==",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "plt.plot(losses)\n",
     "plt.title(\"Training loss\");"
@@ -746,9 +1280,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 47,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "plt.plot(accuracies, label='train')\n",
     "plt.plot(accuracies_test, label='test')\n",
@@ -759,9 +1304,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 48,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 1000x400 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "plot_prediction(model, sample_idx=4)"
    ]
@@ -781,11 +1337,209 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 49,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Top 9 worst predictions (highest loss):\n",
+      "------------------------------------------------------------\n",
+      "Rank   Sample   True   Pred   Loss       Confidence\n",
+      "------------------------------------------------------------\n",
+      "1      107      5      9      4.5113     78.70%\n",
+      "2      99       4      1      3.6421     57.34%\n",
+      "3      180      6      0      3.1957     47.22%\n",
+      "4      44       6      0      2.7955     77.66%\n",
+      "5      69       8      5      2.6354     53.45%\n",
+      "6      105      1      9      2.4278     52.84%\n",
+      "7      123      8      2      2.4056     74.78%\n",
+      "8      26       8      9      2.1871     48.06%\n",
+      "9      252      8      9      1.8069     34.84%\n",
+      "------------------------------------------------------------\n",
+      "\n",
+      "======================================================================\n",
+      "DISPLAY WORST PREDICTIONS\n",
+      "======================================================================\n",
+      "\n",
+      "--- Worst Prediction #1 (Sample 107) ---\n",
+      "True class: 5, Predicted: 9\n",
+      "Loss: 4.5113\n",
+      "\n",
+      "--- Worst Prediction #2 (Sample 99) ---\n",
+      "True class: 4, Predicted: 1\n",
+      "Loss: 3.6421\n",
+      "\n",
+      "--- Worst Prediction #3 (Sample 180) ---\n",
+      "True class: 6, Predicted: 0\n",
+      "Loss: 3.1957\n",
+      "\n",
+      "--- Worst Prediction #4 (Sample 44) ---\n",
+      "True class: 6, Predicted: 0\n",
+      "Loss: 2.7955\n",
+      "\n",
+      "--- Worst Prediction #5 (Sample 69) ---\n",
+      "True class: 8, Predicted: 5\n",
+      "Loss: 2.6354\n",
+      "\n",
+      "--- Worst Prediction #6 (Sample 105) ---\n",
+      "True class: 1, Predicted: 9\n",
+      "Loss: 2.4278\n",
+      "\n",
+      "--- Worst Prediction #7 (Sample 123) ---\n",
+      "True class: 8, Predicted: 2\n",
+      "Loss: 2.4056\n",
+      "\n",
+      "--- Worst Prediction #8 (Sample 26) ---\n",
+      "True class: 8, Predicted: 9\n",
+      "Loss: 2.1871\n",
+      "\n",
+      "--- Worst Prediction #9 (Sample 252) ---\n",
+      "True class: 8, Predicted: 9\n",
+      "Loss: 1.8069\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 1000x400 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 1000x400 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 1000x400 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAxcAAAGJCAYAAAD4084mAAAAOnRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjEwLjgsIGh0dHBzOi8vbWF0cGxvdGxpYi5vcmcvwVt1zgAAAAlwSFlzAAAPYQAAD2EBqD+naQAAVg9JREFUeJzt3X98zfX///H72djZDPNrvxvz+zfDskYiZCSlEkmZn6Xm7den3rV6Z/TDqEhFRKFfIir9EELo7Y0wTeRX8vONGcnG1MbO6/tH3523Y2c489o5G7fr5fK6XLye5/l6PR/ndeb1Oo/zej6fL4thGIYAAAAA4Bp5eToAAAAAANcHkgsAAAAApiC5AAAAAGAKkgsAAAAApiC5AAAAAGAKkgsAAAAApiC5AAAAAGAKkgsAAAAApiC5AAAAAGAKkgtcs3bt2qldu3aeDqNIFfY9HjhwQBaLRa+99pppsaxevVoWi0WrV682bZ8AUNz069dPZcuWNXWfFotFQ4cOvWK9OXPmyGKx6MCBA/ayS68Deef3OXPmXHXbY8aMcS1gE2zcuFE+Pj46ePCg29u+nH79+ikyMtKhzOxjVFK/n/z+++/y9/fXt99+6+lQCoXkohizWCxXtfAlE4Uxf/58xcbGyt/fXxUqVFCrVq30/fffezosoMT45Zdf9PDDDys8PFxWq1VhYWHq06ePfvnll2va77hx47Ro0SJzgryCdevWacyYMTp9+rRb2rveffvttx5JIC7nueeeU+/evVWtWjVPh1IkduzYoTFjxjgkgsXZ6dOn9eijjyowMFD+/v66/fbbtWXLFoc6lStX1qBBg/T88897KMprU8rTAaBgH374ocP6Bx98oOXLl+crr1+/vjvDyue7777zaPtw3ZgxY/TCCy+oR48e6tevn86fP6/t27fryJEjng4NKBE+//xz9e7dW5UqVdLAgQNVvXp1HThwQO+9954WLlyoefPm6d577y3UvseNG6cePXqoe/fu5gbtxLp16zR27Fj169dPFSpUKPL2SopHHnlEDz74oKxWa4F1qlWrpj///FOlS5e2l3377beaOnWq0wTjzz//VKlS7v3alZqaqhUrVmjdunVubbewCnOMduzYobFjx6pdu3b57oQUt+8nNptNXbt21datW/XUU0+pSpUqevvtt9WuXTulpKSodu3a9rpDhgzRm2++qe+//17t27f3YNSuI7koxh5++GGH9Q0bNmj58uX5yi917tw5lSlTpihDc+Dj4+O2tnDtNmzYoBdeeEETJ07UyJEjPR0OUOL89ttveuSRR1SjRg398MMPCgwMtL82fPhwtWnTRo888oh+/vln1ahRw4ORFi9//fWXfHx85OVV/DtNeHt7y9vb+7J1LBaLfH19r3qfrtQ1y+zZs1W1alXdcsstpu0zKytL/v7+pu3vYmYfo+L2/WThwoVat26dFixYoB49ekiSevbsqTp16igpKUlz5861161fv74aNWqkOXPmlLjkovj/D8dltWvXTo0aNVJKSopuu+02lSlTRs8++6ykgvsuRkZGql+/fg5lp0+f1ogRIxQRESGr1apatWppwoQJstlsVxXDxX0a88YEfPrppxo7dqzCw8NVrlw59ejRQxkZGcrOztaIESMUFBSksmXLqn///srOznbY5+zZs9W+fXsFBQXJarWqQYMGmjZtWr62bTabxowZo7CwMJUpU0a33367duzYYfp7vFROTo5Gjx6tFi1aKCAgQP7+/mrTpo1WrVpV4Davv/66qlWrJj8/P7Vt21bbt2/PV2fXrl3q0aOHKlWqJF9fX0VHR+urr766Yjznzp3Trl27dPLkySvWnTx5skJCQjR8+HAZhqGzZ89ecRsA//Pqq6/q3LlzmjFjhkNiIUlVqlTRO++8o6ysLL3yyiv2cmf9y6W/7yJaLBb7usViUVZWlt5//31719e8c1le3V27dqlnz54qX768KleurOHDh+uvv/6y7+NyYwEuvi6MGTNGTz31lCSpevXq9vYu173k4mtOq1at5Ofnp+rVq2v69OkO9fKuA/PmzdO//vUvhYeHq0yZMsrMzJQkLViwQC1atJCfn5+qVKmihx9+uMA7p/v27VNcXJz8/f0VFhamF154QYZhONR57bXX1KpVK1WuXFl+fn5q0aKFFi5cWOD7+Pjjj1W3bl35+vqqRYsW+uGHHxxedzbm4lKXHud+/fpp6tSpkhy7Nedxdk0+cuSIBgwYoODgYFmtVjVs2FCzZs3K19Zbb72lhg0bqkyZMqpYsaKio6MdvogWZNGiRWrfvr1DHNLf3wPuuusufffdd4qKipKvr68aNGigzz//3OlxWLNmjZ544gkFBQXppptusr++ZMkStWnTRv7+/ipXrpy6du3qtFvgokWL1KhRI/n6+qpRo0b64osvnMZb0DEaOHCgwsLCZLVaVb16dT3++OPKycnRnDlz9MADD0iSbr/99nzdxZ2NuUhPT9fAgQMVHBwsX19fNW3aVO+//75DnYvHS86YMUM1a9aU1WrVzTffrE2bNjnUPX/+vHbt2qVjx445fU8XW7hwoYKDg3XffffZywIDA9WzZ099+eWX+b4L3XHHHfr666/z/b0Xd9y5uA78/vvv6tKlix588EE9/PDDCg4Odmn7c+fOqW3btjpy5Igee+wxVa1aVevWrVNiYqKOHTumyZMnFyqu5ORk+fn56ZlnntHevXv11ltvqXTp0vLy8tIff/yhMWPGaMOGDZozZ46qV6+u0aNH27edNm2aGjZsqLvvvlulSpXS119/rSeeeEI2m00JCQn2eomJiXrllVfUrVs3xcXFaevWrYqLi3O40BbFe8zMzNS7776r3r17a/DgwTpz5ozee+89xcXFaePGjYqKinKo/8EHH+jMmTNKSEjQX3/9pTfeeEPt27fXtm3b7J/XL7/8otatWys8PFzPPPOM/P399emnn6p79+767LPPLtvFYuPGjbr99tuVlJR0xf6+K1euVKtWrfTmm2/qpZde0u+//66QkBA999xzVzXQEbjRff3114qMjFSbNm2cvn7bbbcpMjJSixcvdnnfH374oQYNGqSWLVvq0UcflSTVrFnToU7Pnj0VGRmp5ORkbdiwQW+++ab++OMPffDBBy61dd9992nPnj365JNP9Prrr6tKlSqSlC9hutQff/yhO++8Uz179lTv3r316aef6vHHH5ePj48GDBjgUPfFF1+Uj4+PnnzySWVnZ8vHx0dz5sxR//79dfPNNys5OVnHjx/XG2+8of/85z/66aefHLpn5ebmqnPnzrrlllv0yiuvaOnSpUpKStKFCxf0wgsv2Ou98cYbuvvuu9WnTx/l5ORo3rx5euCBB/TNN9+oa9euDjGtWbNG8+fP17Bhw2S1WvX222+rc+fO2rhxoxo1auTSMbzYY489pqNHjzrtvuzM8ePHdcstt9gHmQcGBmrJkiUaOHCgMjMzNWLECEnSzJkzNWzYMPXo0cOeSP7888/68ccf9dBDDxW4/yNHjujQoUNq3ry509d//fVX9erVS0OGDFF8fLxmz56tBx54QEuXLtUdd9zhUPeJJ55QYGCgRo8eraysLEl//63Gx8crLi5OEyZM0Llz5zRt2jTdeuut+umnn+zJ9Hfffaf7779fDRo0UHJysn7//Xf179/fIUkpyNGjR9WyZUv7OIV69erpyJEjWrhwoc6dO6fbbrtNw4YN05tvvqlnn33W3k28oO7if/75p9q1a6e9e/dq6NChql69uhYsWKB+/frp9OnTGj58uEP9uXPn6syZM3rsscdksVj0yiuv6L777tO+ffvs3eGOHDmi+vXrKz4+/oqD+3/66Sc1b9483927li1basaMGdqzZ48aN25sL2/RooVef/11/fLLL9f0t+l2BkqMhIQE49KPrG3btoYkY/r06fnqSzKSkpLylVerVs2Ij4+3r7/44ouGv7+/sWfPHod6zzzzjOHt7W0cOnTosnG1bdvWaNu2rX191apVhiSjUaNGRk5Ojr28d+/ehsViMbp06eKwfWxsrFGtWjWHsnPnzuVrJy4uzqhRo4Z9PS0tzShVqpTRvXt3h3pjxowxJBXpe7xw4YKRnZ3tUOePP/4wgoODjQEDBtjL9u/fb0gy/Pz8jP/+97/28h9//NGQZIwcOdJe1qFDB6Nx48bGX3/9ZS+z2WxGq1atjNq1a9vL8o7vqlWr8pU5+7wvdurUKUOSUblyZaNs2bLGq6++asyfP9/o3LlzgX9HAP7n9OnThiTjnnvuuWy9u+++25BkZGZmGoZhGPHx8fnOc4ZhGElJSfnO6/7+/g7nr0vr3n333Q7lTzzxhCHJ2Lp1q2EY/zvvzJ49O98+Lj1PvPrqq4YkY//+/Zd9P3nyrjkTJ060l2VnZxtRUVFGUFCQ/Zyfd06qUaOGw/k8JyfHCAoKMho1amT8+eef9vJvvvnGkGSMHj3aXhYfH29IMv7xj3/Yy2w2m9G1a1fDx8fHOHHihL380mtGTk6O0ahRI6N9+/b53r8kY/PmzfaygwcPGr6+vsa9995rL5s9e3a+43LpdcDZcXZ2nb647YuP/cCBA43Q0FDj5MmTDvUefPBBIyAgwP6e7rnnHqNhw4ZO93k5K1asMCQZX3/9db7XqlWrZkgyPvvsM3tZRkaGERoaajRr1sxelnccbr31VuPChQv28jNnzhgVKlQwBg8e7LDftLQ0IyAgwKE8KirKCA0NNU6fPm0v++677wxJ+f5PXHqM+vbta3h5eRmbNm3K9x5sNpthGIaxYMGCfNfEPJd+ZpMnTzYkGR999JG9LCcnx4iNjTXKli1r//+a99lWrlzZOHXqlL3ul19+me+Y5tV19n/2Uv7+/g7fEfIsXrzYkGQsXbrUoXzdunWGJGP+/PlX3HdxQreo64DValX//v0Lvf2CBQvUpk0bVaxYUSdPnrQvHTt2VG5ubr7bxVerb9++DgPdYmJiZBhGvl+2YmJidPjwYV24cMFe5ufnZ/93RkaGTp48qbZt22rfvn3KyMiQ9Pcv8BcuXNATTzzhsL9//OMfRf4evb297X05bTabTp06pQsXLig6OjrfrA+S1L17d4WHh9vXW7ZsqZiYGPs0c6dOndL333+vnj176syZM/b4fv/9d8XFxenXX3+97GDrdu3ayTCMK961yOsC9fvvv+vdd9/Vk08+qZ49e2rx4sVq0KCBXnrpJZeOA3CjOXPmjCSpXLlyl62X93peNyAzXXz3VvrfOc9d01aWKlVKjz32mH3dx8dHjz32mNLT05WSkuJQNz4+3uF8vnnzZqWnp+uJJ55w6F/ftWtX1atXz+ndnovvqOb9yp+Tk6MVK1bYyy9u448//lBGRobatGnj9HwcGxurFi1a2NerVq2qe+65R8uWLVNubu7VHoZrYhiGPvvsM3Xr1k2GYThcl+Li4pSRkWGPvUKFCvrvf/+brzvOlfz++++SpIoVKzp9PSwszOGOePny5dW3b1/99NNPSktLc6g7ePBghzEoy5cv1+nTp9W7d2+H2L29vRUTE2PvInzs2DGlpqYqPj5eAQEB9u3vuOMONWjQ4LLx22w2LVq0SN26dVN0dHS+1y/t6nU1vv32W4WEhKh37972stKlS2vYsGE6e/as1qxZ41C/V69eDscv727lvn377GWRkZEyDOOqpiT+888/nU4SkPd/4c8//3Qoz2v7aro8Fyd0i7oOhIeHX9OgpV9//VU///xzgbfC09PTC7XfqlWrOqznnVgiIiLyldtsNmVkZKhy5cqSpP/85z9KSkrS+vXrde7cOYf6GRkZCggIsM/ZXatWLYfXK1WqlO9kWhTv8f3339fEiRO1a9cunT9/3l5evXr1fHUvngEiT506dfTpp59Kkvbu3SvDMPT8888XOPVcenq6Q4JSGHkX4NKlS9sHk0mSl5eXevXqpaSkJB06dCjfZwfgb3lJQ16SUZCrTUIK49LzSc2aNeXl5eW2qTjDwsLyDeitU6eOpL/7ql88ePjS82Heebtu3br59luvXj2tXbvWoczLyyvfoPiL28rzzTff6KWXXlJqaqpDv3VnX0ALOh+fO3dOJ06cUEhISL7XzXbixAmdPn1aM2bM0IwZM5zWybsuPf3001qxYoVatmypWrVqqVOnTnrooYfUunXrq2rLKKC/fq1atfIdn4uP7cXH4dLP8ddff5WkAgcaly9fXtL/Pm9nx7xu3bpOk788J06cUGZmpqndgQ4ePKjatWvn65aU143q0meBXHotzPtu8ccffxSqfT8/v3zjKiTZu3JfnCRL//vsCpNIeRLJxXXg0j/GK7n0lxmbzaY77rhD//znP53WzzvZuKqgmTYKKs/7T/Tbb7+pQ4cOqlevniZNmqSIiAj5+Pjo22+/1euvv16oAdhmv8ePPvpI/fr1U/fu3fXUU08pKChI3t7eSk5O1m+//Vao+CTpySefVFxcnNM6lyZRhZE3ULxChQr5PoegoCBJf580SS4A5wICAhQaGqqff/75svV+/vlnhYeH279kFfTlwIxfyi/dd1G25SpXr0+F8e9//1t33323brvtNr399tsKDQ1V6dKlNXv27Ksa9OwJeef8hx9+WPHx8U7rNGnSRNLfX3x3796tb775RkuXLtVnn32mt99+W6NHj9bYsWMLbCPvx7rCfhG+2KWfY178H374odNkzN1T7haVK31fcVVoaKjTgd95ZWFhYQ7leZ9d3niokuL6+PThVMWKFfM9GCknJyffH3bNmjV19uxZdezY0Y3RFezrr79Wdna2vvrqK4cvuZfOxJT3QKC9e/c6/Kry+++/5zuZmv0eFy5cqBo1aujzzz93uJAnJSU5rZ/3K8/F9uzZYx/wlvfLXOnSpYv0c/Dy8lJUVJQ2bdqknJwchzteR48elXTlwZzAje6uu+7SzJkztXbtWt166635Xv/3v/+tAwcOOHQdcnY+lvL/Uipd+VfKX3/91eGct3fvXtlsNvv5JO/X1UvbK0xbzhw9ejTfdKR79uyRJKczYl0s77y9e/fufL967969O9+D3mw2m/bt2+fwA9ClbX322Wfy9fXVsmXLHLqczJ4922kMBZ2Py5Qpc83nv6s9noGBgSpXrpxyc3Ov6pzv7++vXr16qVevXsrJydF9992nl19+WYmJiQVO31qvXj1J0v79+52+nnfH/OKYr/ZzzJtkICgo6LLx532ezo757t27L9tGYGCgypcv73RmxYu58jdcrVo1/fzzz7LZbA53L3bt2uUQb1GJiorSv//973zt//jjjypTpky+HzrzPjtPP8/MVYy5uI7VrFkz31iCGTNm5Pv1qmfPnlq/fr2WLVuWbx+nT592GAvhDnm/FFz8y0BGRka+C0WHDh1UqlSpfFPUTpkyJd8+zX6PzmL88ccftX79eqf1Fy1a5DBmYuPGjfrxxx/VpUsXSX+foNu1a6d33nnH6a8aJ06cuGw8rkxF26tXL+Xm5jpMvffXX3/p448/VoMGDfL9cgLA0VNPPSU/Pz899thj9n7teU6dOqUhQ4aoTJky9mlepb/PxxkZGQ53PI4dO+Z0Sk5/f//LPjE7b7rTPG+99ZYk2c8n5cuXV5UqVfKd/99++22nbUn5E5HLuXDhgt555x37ek5Ojt555x0FBgY6jGVwJjo6WkFBQZo+fbpD95AlS5Zo586d+WZ2khzP6YZhaMqUKSpdurQ6dOgg6e/zscVicbi2HThwoMCnnK9fv96hO87hw4f15ZdfqlOnTld8tsWVXO3x9Pb21v3336/PPvvM6Zfni8/5l/6N+fj4qEGDBjIMw6FL7qXCw8MVERGhzZs3O3396NGjDn9/mZmZ+uCDDxQVFXXFrmFxcXEqX768xo0b5zSGvPhDQ0MVFRWl999/3z5eUvp7zMaOHTsu24aXl5e6d++ur7/+2ul7yLv+uvI3fOeddyotLU3z58+3l124cEFvvfWWypYtq7Zt215xH5dyZSraHj166Pjx4w5T/p48eVILFixQt27d8o3HSElJUUBAgBo2bOhyXJ7EnYvr2KBBgzRkyBDdf//9uuOOO7R161YtW7Ys3+21p556Sl999ZXuuusu9evXTy1atFBWVpa2bdumhQsX6sCBA269JdepUyf5+PioW7dueuyxx3T27FnNnDlTQUFBDv95g4ODNXz4cE2cOFF33323OnfurK1bt2rJkiWqUqWKw68ZZr/Hu+66S59//rnuvfdede3aVfv379f06dPVoEEDp8+NqFWrlm699VY9/vjjys7O1uTJk1W5cmWHblpTp07VrbfeqsaNG2vw4MGqUaOGjh8/rvXr1+u///2vtm7dWmA8rkxF+9hjj+ndd99VQkKC9uzZo6pVq+rDDz/UwYMH9fXXX1/1MQBuVLVr19b777+vPn36qHHjxvme0H3y5El98sknDlPIPvjgg3r66ad17733atiwYfZpO+vUqZOv33mLFi20YsUKTZo0SWFhYapevbpiYmLsr+/fv99+zlu/fr0++ugjPfTQQ2ratKm9zqBBgzR+/HgNGjRI0dHR+uGHH+y/Sl/aliQ999xzevDBB1W6dGl169btsg9JCwsL04QJE3TgwAHVqVNH8+fPV2pqqmbMmOEwiYczpUuX1oQJE9S/f3+1bdtWvXv3tk9FGxkZme/Bnr6+vlq6dKni4+MVExOjJUuWaPHixXr22Wftdxm6du2qSZMmqXPnznrooYeUnp6uqVOnqlatWk67rzVq1EhxcXEOU9FKumwXo6uVdzyHDRumuLg4eXt768EHH3Rad/z48Vq1apViYmI0ePBgNWjQQKdOndKWLVu0YsUKnTp1StLf18SQkBC1bt1awcHB2rlzp6ZMmaKuXbtecUzPPffcoy+++CLfHQrp7+7AAwcO1KZNmxQcHKxZs2bp+PHjBd7xuVj58uU1bdo0PfLII2revLkefPBBBQYG6tChQ1q8eLFat25tTwqTk5PVtWtX3XrrrRowYIBOnTplf27HlZ6zNG7cOH333Xdq27atHn30UdWvX1/Hjh3TggULtHbtWlWoUEFRUVHy9vbWhAkTlJGRIavVan9O1qUeffRRvfPOO+rXr59SUlIUGRmphQsX6j//+Y8mT55cqDFSrkxF26NHD91yyy3q37+/duzYYX9Cd25urtO/v+XLl6tbt24lbswFU9GWIAVNRVvQFHW5ubnG008/bVSpUsUoU6aMERcXZ+zduzffVLSG8fe0comJiUatWrUMHx8fo0qVKkarVq2M1157zWE6WWcKmop2wYIFDvXyprS7dEq5vOkVL55W8KuvvjKaNGli+Pr6GpGRkcaECROMWbNm5Zsa8MKFC8bzzz9vhISEGH5+fkb79u2NnTt3GpUrVzaGDBlSZO/RZrMZ48aNM6pVq2ZYrVajWbNmxjfffJNvusm8KepeffVVY+LEiUZERIRhtVqNNm3a2KeNvNhvv/1m9O3b1wgJCTFKly5thIeHG3fddZexcOHCfMe3MFPR5jl+/LgRHx9vVKpUybBarUZMTEy+KfAAXN7PP/9s9O7d2wgNDTVKly5thISEGL179za2bdvmtP53331nNGrUyPDx8THq1q1rfPTRR06not21a5dx2223GX5+fg5TXObV3bFjh9GjRw+jXLlyRsWKFY2hQ4c6TOtqGH9PzTpw4EAjICDAKFeunNGzZ08jPT3d6XnixRdfNMLDww0vL68rTkubd83ZvHmzERsba/j6+hrVqlUzpkyZ4lCvoOtAnvnz5xvNmjUzrFarUalSJaNPnz4O03Ubxt9T0fr7+xu//fab0alTJ6NMmTJGcHCwkZSUZOTm5jrUfe+994zatWsbVqvVqFevnjF79mynx1aSkZCQYHz00Uf2+s2aNcs3jWlhp6K9cOGC8Y9//MMIDAw0LBaLQ/vOjv3x48eNhIQEIyIiwv431KFDB2PGjBn2Ou+8845x2223GZUrVzasVqtRs2ZN46mnnjIyMjKcHtuLbdmyxZBk/Pvf/3Yor1atmtG1a1dj2bJlRpMmTezH7Wqv23lWrVplxMXFGQEBAYavr69Rs2ZNo1+/fg5T/RqGYXz22WdG/fr1DavVajRo0MD4/PPPnU7P7OwYHTx40Ojbt68RGBhoWK1Wo0aNGkZCQoLDdPAzZ840atSoYXh7eztcHy/9zAzj72Pev39/o0qVKoaPj4/RuHHjfNM2X3ztvtSlMboyFa1h/D0l/MCBA43KlSsbZcqUMdq2bev0+O7cudOQZKxYseKq9lucWAyjhD32D7iC06dPq2LFinrppZf03HPPeTocADDFmDFjNHbsWJ04ccJjAzzbtWunkydPXrEfPIqPDh06KCwszOHBfpGRkWrUqJG++eYbD0aGyxkxYoR++OEHpaSklLg7F4y5QIl26ZzQkuxP227Xrp17gwEAoJgZN26c5s+f73RAP4qnvGdRvfTSSyUusZAYc4ESbv78+ZozZ47uvPNOlS1bVmvXrtUnn3yiTp06XfUc4AAAXK9iYmKUk5Pj6TDggsqVK19xPEpxRnKBEq1JkyYqVaqUXnnlFWVmZtoHefOkaQAAAPdjzAUAAAAAUzDmAgAAAIApSC4AAAAAmMLtYy5sNpuOHj2qcuXKlcgR8ACQxzAMnTlzRmFhYfLy4reaosb1AwA8w5XrnduTi6NHjyoiIsLdzQJAkTl8+LBuuukmT4dx3eP6AQCedTXXO7cnF3mPVj98+LDKly/v7uZLrNOnT3s6hALdeuutng7Bqa5du3o6hAJNmDDB0yHABJmZmYqIiLCf11C0uH4AgGe4cr1ze3KRdyu7fPnyXBxcYLPZPB1CgYprdxCr1erpEArE3/71hS467sH1AwA862qud8XzWyEAAACAEofkAgAAAIApSC4AAAAAmMLtYy4AACgqhmHowoULys3N9XQoMIm3t7dKlSrF2CaghCC5AABcF3JycnTs2DGdO3fO06HAZGXKlFFoaKh8fHw8HQqAKyC5AACUeDabTfv375e3t7fCwsLk4+PDL93XAcMwlJOToxMnTmj//v2qXbt2sZ2hEMDfSC4AACVeTk6ObDabIiIiVKZMGU+HAxP5+fmpdOnSOnjwoHJycuTr6+vpkABcBuk/AMBlP/zwg7p166awsDBZLBYtWrToitusXr1azZs3l9VqVa1atTRnzhzT4+JX7esTnytQcvC/FQDgsqysLDVt2lRTp069qvr79+9X165ddfvttys1NVUjRozQoEGDtGzZsiKOFADgTnSLAgC4rEuXLurSpctV158+fbqqV6+uiRMnSpLq16+vtWvX6vXXX1dcXFxRhQkAcDPuXAAAitz69evVsWNHh7K4uDitX7++wG2ys7OVmZnpsAAAijfuXAAAilxaWpqCg4MdyoKDg5WZmak///xTfn5++bZJTk7W2LFjr7ntyGcWX/M+rtaB8V1d3qZdu3aKiorS5MmTzQ8IuAx3/d8ozP8LlFyFunMxdepURUZGytfXVzExMdq4caPZcQEAbnCJiYnKyMiwL4cPH/Z0SB6R92BAACgJXE4u5s+fr1GjRikpKUlbtmxR06ZNFRcXp/T09KKIDwBwHQgJCdHx48cdyo4fP67y5cs7vWshSVarVeXLl3dYrjf9+vXTmjVr9MYbb8hischisWjOnDmyWCxasmSJWrRoIavVqrVr16pfv37q3r27w/YjRoxQu3bt7Os2m03JycmqXr26/Pz81LRpUy1cuNC9bwrADc3l5GLSpEkaPHiw+vfvrwYNGmj69OkqU6aMZs2aVRTxAQCuA7GxsVq5cqVD2fLlyxUbG+uhiIqHN954Q7GxsRo8eLCOHTumY8eOKSIiQpL0zDPPaPz48dq5c6eaNGlyVftLTk7WBx98oOnTp+uXX37RyJEj9fDDD2vNmjVF+TYAwM6lMRc5OTlKSUlRYmKivczLy0sdO3YscFBedna2srOz7esMyAOAku/s2bPau3evfX3//v1KTU1VpUqVVLVqVSUmJurIkSP64IMPJElDhgzRlClT9M9//lMDBgzQ999/r08//VSLF7tvPERxFBAQIB8fH5UpU0YhISGSpF27dkmSXnjhBd1xxx1Xva/s7GyNGzdOK1assCdtNWrU0Nq1a/XOO++obdu25r8BALiES8nFyZMnlZub63RQXt7J8FJmDcgDABQfmzdv1u23325fHzVqlCQpPj5ec+bM0bFjx3To0CH769WrV9fixYs1cuRIvfHGG7rpppv07rvvMg3tZURHR7tUf+/evTp37ly+hCQnJ0fNmjUzMzQAKFCRzxaVmJhov+hIf9+5yLvlCwAomdq1ayfDMAp83dnTt9u1a6effvqpCKO6vvj7+zuse3l55Tvm58+ft//77NmzkqTFixcrPDzcoZ7Vai2iKAHAkUvJRZUqVeTt7e10UF7e7dxLWa1WTmoAABTAx8dHubm5V6wXGBio7du3O5SlpqaqdOnSkqQGDRrIarXq0KFDdIEC4DEuDej28fFRixYtHAbl2Ww2rVy58oYflAcAQGFERkbqxx9/1IEDB3Ty5EnZbDan9dq3b6/Nmzfrgw8+0K+//qqkpCSHZKNcuXJ68sknNXLkSL3//vv67bfftGXLFr311lt6//333fV2ANzgXO4WNWrUKMXHxys6OlotW7bU5MmTlZWVpf79+xdFfAAAXJPi/gCvJ598UvHx8WrQoIH+/PNPzZ4922m9uLg4Pf/88/rnP/+pv/76SwMGDFDfvn21bds2e50XX3xRgYGBSk5O1r59+1ShQgU1b95czz77rLveDoAbnMvJRa9evXTixAmNHj1aaWlpioqK0tKlS/MN8gYAAFdWp06dfDMu9uvXz2ndsWPHXnaSFIvFouHDh2v48OFmhggAV61QA7qHDh2qoUOHmh0LAAAAgBLM5YfoAQAAAIAzJBcAAAAATEFyAQAAAMAUJBcAAAAATEFyAQAAAMAUJBcAAAAATEFyAQAAAMAUJBcAAAAATEFyAQC4vlks7luKucjISE2ePNm+brFYtGjRomvapxn7AHD9KNQTugEAQMl37NgxVaxY8arqjhkzRosWLVJqamqh9wHg+kdyAQBACZKTkyMfHx9T9hUSElIs9gHg+kG3KAAAPKhdu3YaOnSohg4dqoCAAFWpUkXPP/+8DMOQ9HdXphdffFF9+/ZV+fLl9eijj0qS1q5dqzZt2sjPz08REREaNmyYsrKy7PtNT09Xt27d5Ofnp+rVq+vjjz/O1/alXZr++9//qnfv3qpUqZL8/f0VHR2tH3/8UXPmzNHYsWO1detWWSwWWSwWzZkzx+k+tm3bpvbt28vPz0+VK1fWo48+qrNnz9pf79evn7p3767XXntNoaGhqly5shISEnT+/HkTjyoAT+HORQkRFRXl6RAKdPr0aU+H4FS/fv08HQIAXJX3339fAwcO1MaNG7V582Y9+uijqlq1qgYPHixJeu211zR69GglJSVJkn777Td17txZL730kmbNmqUTJ07YE5TZs2dL+vscePToUa1atUqlS5fWsGHDlJ6eXmAMZ8+eVdu2bRUeHq6vvvpKISEh2rJli2w2m3r16qXt27dr6dKlWrFihSQpICAg3z6ysrIUFxen2NhYbdq0Senp6Ro0aJCGDh1qT0YkadWqVQoNDdWqVau0d+9e9erVS1FRUfb3C6DkIrkAAMDDIiIi9Prrr8tisahu3bratm2bXn/9dfuX7fbt2+v//u//7PUHDRqkPn36aMSIEZKk2rVr680331Tbtm01bdo0HTp0SEuWLNHGjRt18803S5Lee+891a9fv8AY5s6dqxMnTmjTpk2qVKmSJKlWrVr218uWLatSpUpdthvU3Llz9ddff+mDDz6Qv7+/JGnKlCnq1q2bJkyYoODgYElSxYoVNWXKFHl7e6tevXrq2rWrVq5cSXIBXAfoFgUAgIfdcsstslw021RsbKx+/fVX5ebmSpKio6Md6m/dulVz5sxR2bJl7UtcXJxsNpv279+vnTt3qlSpUmrRooV9m3r16qlChQoFxpCamqpmzZrZE4vC2Llzp5o2bWpPLCSpdevWstls2r17t72sYcOG8vb2tq+HhoZe9q4KgJKDOxcAABRzF39Zl/7uwvTYY49p2LBh+epWrVpVe/bscbkNPz+/QsfnqtKlSzusWywW2Ww2t7UPoOhw5wIAAA/78ccfHdY3bNig2rVrO/y6f7HmzZtrx44dqlWrVr7Fx8dH9erV04ULF5SSkmLfZvfu3ZcdI9ekSROlpqbq1KlTTl/38fGx30kpSP369bV161aHgeX/+c9/5OXlpbp16152WwDXB5ILAAA87NChQxo1apR2796tTz75RG+99ZaGDx9eYP2nn35a69at09ChQ5Wamqpff/1VX375pYYOHSpJqlu3rjp37qzHHntMP/74o1JSUjRo0KDL3p3o3bu3QkJC1L17d/3nP//Rvn379Nlnn2n9+vWS/p61av/+/UpNTdXJkyeVnZ2dbx99+vSRr6+v4uPjtX37dq1atUr/+Mc/9Mgjj9jHWwC4vpFcAACub4bhvqWQ+vbtqz///FMtW7ZUQkKChg8fbp9y1pkmTZpozZo12rNnj9q0aaNmzZpp9OjRCgsLs9eZPXu2wsLC1LZtW91333169NFHFRQUVOA+fXx89N133ykoKEh33nmnGjdurPHjx9vvntx///3q3Lmzbr/9dgUGBuqTTz7Jt48yZcpo2bJlOnXqlG6++Wb16NFDHTp00JQpUwp9bACULBbDuIazYSFkZmYqICBAGRkZKl++vDubLtEiIyM9HUKBiutUtKtXr/Z0CAUqzlML4+pxPnOvyx3vv/76S/v371f16tXl6+vroQgLp127doqKitLkyZM9HUqxVZI/3+Is8pnFbmnnwPiubmkHRceV6x13LgAAAACYguQCAAAAgCmYihYAAA8qzl04AcBV3LkAAAAAYAqSCwDAdcPNc5TATfhcgZKD5AIAUOLlPfH53LlzHo4ERSHvc730yd4Aih/GXAAASjxvb29VqFBB6enpkv5+3oLFYvFwVLhWhmHo3LlzSk9PV4UKFQp8YjmA4oPkAgBwXQgJCZEke4KB60eFChXsny+A4s3l5OKHH37Qq6++qpSUFB07dkxffPGFunfvXgShAQBw9SwWi0JDQxUUFKTz5897OhyYpHTp0tyxAEoQl5OLrKwsNW3aVAMGDNB9991XFDEBAFBo3t7efBkFAA9xObno0qWLunTpctX1s7OzlZ2dbV/PzMx0tUkAAAAAJUCRzxaVnJysgIAA+xIREVHUTQIAAADwgCJPLhITE5WRkWFfDh8+XNRNAgAAAPCAIp8tymq1ymq1FnUzAAAAADyMh+gBAAAAMAXJBQAAAABTuNwt6uzZs9q7d699ff/+/UpNTVWlSpVUtWpVU4MDAAAAUHK4nFxs3rxZt99+u3191KhRkqT4+HjNmTPHtMAAAAAAlCwuJxft2rWTYRhFEQsAAACAEowxFwAAAABMQXIBAAAAwBQkFwAAAABMQXIBAAAAwBQkFwCAQpk6daoiIyPl6+urmJgYbdy48bL1J0+erLp168rPz08REREaOXKk/vrrLzdFCwBwB5ILAIDL5s+fr1GjRikpKUlbtmxR06ZNFRcXp/T0dKf1586dq2eeeUZJSUnauXOn3nvvPc2fP1/PPvusmyMHABQlkgsAgMsmTZqkwYMHq3///mrQoIGmT5+uMmXKaNasWU7rr1u3Tq1bt9ZDDz2kyMhIderUSb17977i3Q4AQMlCcgEAcElOTo5SUlLUsWNHe5mXl5c6duyo9evXO92mVatWSklJsScT+/bt07fffqs777yzwHays7OVmZnpsAAAijeXH6IHALixnTx5Urm5uQoODnYoDw4O1q5du5xu89BDD+nkyZO69dZbZRiGLly4oCFDhly2W1RycrLGjh1rauwAgKLFnQsAQJFbvXq1xo0bp7fffltbtmzR559/rsWLF+vFF18scJvExERlZGTYl8OHD7sxYgBAYXDnAgDgkipVqsjb21vHjx93KD9+/LhCQkKcbvP888/rkUce0aBBgyRJjRs3VlZWlh599FE999xz8vLK/1uX1WqV1Wo1/w0AAIoMdy4AAC7x8fFRixYttHLlSnuZzWbTypUrFRsb63Sbc+fO5UsgvL29JUmGYRRdsAAAt+LOxSVOnz7t6RCcOnjwoKdDKFB8fLynQ3AqKirK0yEA161Ro0YpPj5e0dHRatmypSZPnqysrCz1799fktS3b1+Fh4crOTlZktStWzdNmjRJzZo1U0xMjPbu3avnn39e3bp1sycZAICSj+QCAOCyXr166cSJExo9erTS0tIUFRWlpUuX2gd5Hzp0yOFOxb/+9S9ZLBb961//0pEjRxQYGKhu3brp5Zdf9tRbAAAUAZILAEChDB06VEOHDnX62urVqx3WS5UqpaSkJCUlJbkhMgCAp5BcAABuHBaLe9phHAmAGxQDugEAAACYguQCAAAAgClILgAAAACYguQCAAAAgClILgAAAACYguQCAAAAgClILgAAAACYguQCAAAAgClILgAAAACYguQCAAAAgClILgAAAACYwqXkIjk5WTfffLPKlSunoKAgde/eXbt37y6q2AAAAACUIC4lF2vWrFFCQoI2bNig5cuX6/z58+rUqZOysrKKKj4AAAAAJUQpVyovXbrUYX3OnDkKCgpSSkqKbrvtNlMDAwAAAFCyuJRcXCojI0OSVKlSpQLrZGdnKzs7276emZl5LU0CAAAAKKYKPaDbZrNpxIgRat26tRo1alRgveTkZAUEBNiXiIiIwjYJAAAAoBgrdHKRkJCg7du3a968eZetl5iYqIyMDPty+PDhwjYJAAAAoBgrVLeooUOH6ptvvtEPP/ygm2666bJ1rVarrFZroYIDAAAAUHK4lFwYhqF//OMf+uKLL7R69WpVr169qOICAAAAUMK4lFwkJCRo7ty5+vLLL1WuXDmlpaVJkgICAuTn51ckAQIAAAAoGVwaczFt2jRlZGSoXbt2Cg0NtS/z588vqvgAAAAAlBAud4sCAAAAAGcKPVsUAAAAAFyM5AIAAACAKUguAAAAAJiC5AIAAACAKUguAAAAAJiC5AIAAACAKUguAAAAAJiC5AIAAACAKUguAAAAAJiC5AIAAACAKUguAAAAAJiC5AIAAACAKUp5OoDiZvXq1Z4Owalq1ap5OoQCpaamejoEpypUqODpEArUvXt3T4dQoDlz5ng6BAAAUEJx5wIAAACAKUguAAAAAJiC5AIAAACAKUguAAAAAJiC5AIAAACAKUguAAAAAJiC5AIAAACAKUguAAAAAJiC5AIAAACAKUguAAAAAJiC5AIAAACAKUguAAAAAJiC5AIAUChTp05VZGSkfH19FRMTo40bN162/unTp5WQkKDQ0FBZrVbVqVNH3377rZuiBQC4QylPBwAAKHnmz5+vUaNGafr06YqJidHkyZMVFxen3bt3KygoKF/9nJwc3XHHHQoKCtLChQsVHh6ugwcPqkKFCu4PHgBQZEguAAAumzRpkgYPHqz+/ftLkqZPn67Fixdr1qxZeuaZZ/LVnzVrlk6dOqV169apdOnSkqTIyEh3hgwAcAOXukVNmzZNTZo0Ufny5VW+fHnFxsZqyZIlRRUbAKAYysnJUUpKijp27Ggv8/LyUseOHbV+/Xqn23z11VeKjY1VQkKCgoOD1ahRI40bN065ubkFtpOdna3MzEyHBQBQvLmUXNx0000aP368UlJStHnzZrVv31733HOPfvnll6KKDwBQzJw8eVK5ubkKDg52KA8ODlZaWprTbfbt26eFCxcqNzdX3377rZ5//nlNnDhRL730UoHtJCcnKyAgwL5ERESY+j4AAOZzqVtUt27dHNZffvllTZs2TRs2bFDDhg2dbpOdna3s7Gz7Or88AcCNx2azKSgoSDNmzJC3t7datGihI0eO6NVXX1VSUpLTbRITEzVq1Cj7emZmJgkGABRzhR5zkZubqwULFigrK0uxsbEF1ktOTtbYsWML2wwAoJipUqWKvL29dfz4cYfy48ePKyQkxOk2oaGhKl26tLy9ve1l9evXV1pamnJycuTj45NvG6vVKqvVam7wAIAi5fJUtNu2bVPZsmVltVo1ZMgQffHFF2rQoEGB9RMTE5WRkWFfDh8+fE0BAwA8y8fHRy1atNDKlSvtZTabTStXrizwx6bWrVtr7969stls9rI9e/YoNDTUaWIBACiZXE4u6tatq9TUVP344496/PHHFR8frx07dhRY32q12geA5y0AgJJt1KhRmjlzpt5//33t3LlTjz/+uLKysuyzR/Xt21eJiYn2+o8//rhOnTql4cOHa8+ePVq8eLHGjRunhIQET70FAEARcLlblI+Pj2rVqiVJatGihTZt2qQ33nhD77zzjunBAQCKp169eunEiRMaPXq00tLSFBUVpaVLl9oHeR86dEheXv/7/SoiIkLLli3TyJEj1aRJE4WHh2v48OF6+umnPfUWAABF4Jqfc2Gz2RwGbAMAbgxDhw7V0KFDnb62evXqfGWxsbHasGFDEUcFAPAkl5KLxMREdenSRVWrVtWZM2c0d+5crV69WsuWLSuq+AAAAACUEC4lF+np6erbt6+OHTumgIAANWnSRMuWLdMdd9xRVPEBAAAAKCFcSi7ee++9oooDAAAAQAnn8mxRAAAAAOAMyQUAAAAAU5BcAAAAADAFyQUAAAAAU5BcAAAAADAFyQUAAAAAU5BcAAAAADAFyQUAAAAAU5BcAAAAADAFyQUAAAAAU5BcAAAAADAFyQUAAAAAU5TydADFTWpqqqdDcOrgwYOeDqFAq1ev9nQITp0+fdrTIRSoe/fung6hQIsWLfJ0CE4V52MGAAD+xp0LAAAAAKYguQAAAABgCpILAAAAAKYguQAAAABgCpILAAAAAKYguQAAAABgCpILAAAAAKYguQAAAABgCpILAAAAAKYguQAAAABgCpILAAAAAKYguQAAAABgCpILAAAAAKYguQAAAABgimtKLsaPHy+LxaIRI0aYFA4AAACAkqrQycWmTZv0zjvvqEmTJmbGAwAAAKCEKlRycfbsWfXp00czZ85UxYoVzY4JAAAAQAlUqOQiISFBXbt2VceOHa9YNzs7W5mZmQ4LAAAAgOtPKVc3mDdvnrZs2aJNmzZdVf3k5GSNHTvW5cAAAAAAlCwu3bk4fPiwhg8fro8//li+vr5XtU1iYqIyMjLsy+HDhwsVKAAAAIDizaU7FykpKUpPT1fz5s3tZbm5ufrhhx80ZcoUZWdny9vb22Ebq9Uqq9VqTrQAAAAAii2XkosOHTpo27ZtDmX9+/dXvXr19PTTT+dLLAAAAADcOFxKLsqVK6dGjRo5lPn7+6ty5cr5ygEAAADcWHhCNwAAAABTuDxb1KVWr15tQhgAAAAASjruXAAAAAAwBckFAAAAAFOQXAAAAAAwBckFAAAAAFOQXAAAAAAwBckFAAAAAFOQXAAACmXq1KmKjIyUr6+vYmJitHHjxqvabt68ebJYLOrevXvRBggAcDuSCwCAy+bPn69Ro0YpKSlJW7ZsUdOmTRUXF6f09PTLbnfgwAE9+eSTatOmjZsiBQC4E8kFAMBlkyZN0uDBg9W/f381aNBA06dPV5kyZTRr1qwCt8nNzVWfPn00duxY1ahRw43RAgDcheQCAOCSnJwcpaSkqGPHjvYyLy8vdezYUevXry9wuxdeeEFBQUEaOHDgVbWTnZ2tzMxMhwUAULyRXAAAXHLy5Enl5uYqODjYoTw4OFhpaWlOt1m7dq3ee+89zZw586rbSU5OVkBAgH2JiIi4prgBAEWP5AIAUKTOnDmjRx55RDNnzlSVKlWuervExERlZGTYl8OHDxdhlAAAM5TydADFTVRUlKdDKHEOHDjg6RCcateunadDKFBkZKSnQyjQ6dOnPR0CirkqVarI29tbx48fdyg/fvy4QkJC8tX/7bffdODAAXXr1s1eZrPZJEmlSpXS7t27VbNmzXzbWa1WWa1Wk6MHABQl7lwAAFzi4+OjFi1aaOXKlfYym82mlStXKjY2Nl/9evXqadu2bUpNTbUvd999t26//XalpqbS3QkAriPcuQAAuGzUqFGKj49XdHS0WrZsqcmTJysrK0v9+/eXJPXt21fh4eFKTk6Wr6+vGjVq5LB9hQoVJClfOQCgZCO5AAC4rFevXjpx4oRGjx6ttLQ0RUVFaenSpfZB3ocOHZKXFzfHAeBGQ3IBACiUoUOHaujQoU5fW7169WW3nTNnjvkBAQA8jp+VAAAAAJiC5AIAAACAKUguAAAAAJiC5AIAAACAKUguAAAAAJiC5AIAAACAKUguAAAAAJiC5AIAAACAKUguAAAAAJiC5AIAAACAKUguAAAAAJjCpeRizJgxslgsDku9evWKKjYAAAAAJUgpVzdo2LChVqxY8b8dlHJ5FwAAAACuQy5nBqVKlVJISMhV18/OzlZ2drZ9PTMz09UmAQAAAJQALo+5+PXXXxUWFqYaNWqoT58+OnTo0GXrJycnKyAgwL5EREQUOlgAAAAAxZdLyUVMTIzmzJmjpUuXatq0adq/f7/atGmjM2fOFLhNYmKiMjIy7Mvhw4evOWgAAAAAxY9L3aK6dOli/3eTJk0UExOjatWq6dNPP9XAgQOdbmO1WmW1Wq8tSgAAAADF3jVNRVuhQgXVqVNHe/fuNSseAAAAACXUNSUXZ8+e1W+//abQ0FCz4gEAAABQQrmUXDz55JNas2aNDhw4oHXr1unee++Vt7e3evfuXVTxAQAAACghXBpz8d///le9e/fW77//rsDAQN16663asGGDAgMDiyo+AAAAACWES8nFvHnziioOAAAAACXcNY25AAAAAIA8JBcAAAAATEFyAQAAAMAUJBcAAAAATEFyAQAAAMAUJBcAAAAATEFyAQAAAMAUJBcAAAAATEFyAQAAAMAUJBcAAAAATEFyAQAAAMAUJBcAAAAATFHK0wEUN927d/d0CE7Fx8d7OoQCjRgxwtMhOLVo0SJPh1CgAwcOeDoEAAAA03HnAgAAAIApSC4AAAAAmILkAgAAAIApSC4AAAAAmILkAgAAAIApSC4AAAAAmILkAgAAAIApSC4AAAAAmILkAgAAAIApSC4AAAAAmILkAgAAAIApSC4AAAAAmILkAgAAAIApSC4AAIUydepURUZGytfXVzExMdq4cWOBdWfOnKk2bdqoYsWKqlixojp27HjZ+gCAksnl5OLIkSN6+OGHVblyZfn5+alx48bavHlzUcQGACim5s+fr1GjRikpKUlbtmxR06ZNFRcXp/T0dKf1V69erd69e2vVqlVav369IiIi1KlTJx05csTNkQMAipJLycUff/yh1q1bq3Tp0lqyZIl27NihiRMnqmLFikUVHwCgGJo0aZIGDx6s/v37q0GDBpo+fbrKlCmjWbNmOa3/8ccf64knnlBUVJTq1aund999VzabTStXrnRz5ACAolTKlcoTJkxQRESEZs+ebS+rXr266UEBAIqvnJwcpaSkKDEx0V7m5eWljh07av369Ve1j3Pnzun8+fOqVKlSgXWys7OVnZ1tX8/MzCx80AAAt3DpzsVXX32l6OhoPfDAAwoKClKzZs00c+bMy26TnZ2tzMxMhwUAUHKdPHlSubm5Cg4OdigPDg5WWlraVe3j6aefVlhYmDp27FhgneTkZAUEBNiXiIiIa4obAFD0XEou9u3bp2nTpql27dpatmyZHn/8cQ0bNkzvv/9+gdtwcQAAXGz8+PGaN2+evvjiC/n6+hZYLzExURkZGfbl8OHDbowSAFAYLnWLstlsio6O1rhx4yRJzZo10/bt2zV9+nTFx8c73SYxMVGjRo2yr2dmZpJgAEAJVqVKFXl7e+v48eMO5cePH1dISMhlt33ttdc0fvx4rVixQk2aNLlsXavVKqvVes3xAgDcx6U7F6GhoWrQoIFDWf369XXo0KECt7FarSpfvrzDAgAouXx8fNSiRQuHwdh5g7NjY2ML3O6VV17Riy++qKVLlyo6OtodoQIA3MylOxetW7fW7t27Hcr27NmjatWqmRoUAKB4GzVqlOLj4xUdHa2WLVtq8uTJysrKUv/+/SVJffv2VXh4uJKTkyX9PSHI6NGjNXfuXEVGRtrHZpQtW1Zly5b12PsAAJjLpeRi5MiRatWqlcaNG6eePXtq48aNmjFjhmbMmFFU8QEAiqFevXrpxIkTGj16tNLS0hQVFaWlS5faB3kfOnRIXl7/uzk+bdo05eTkqEePHg77SUpK0pgxY9wZOgCgCLmUXNx888364osvlJiYqBdeeEHVq1fX5MmT1adPn6KKDwBQTA0dOlRDhw51+trq1asd1g8cOFD0AQEAPM6l5EKS7rrrLt11111FEQsAAACAEsylAd0AAAAAUBCSCwAAAACmILkAAAAAYAqSCwAAAACmILkAAAAAYAqSCwAAAACmILkAAAAAYAqSCwAAAACmILkAAAAAYAqSCwAAAACmILkAAAAAYAqSCwAAAACmKOXpAHB1xowZ4+kQCtSvXz9Ph+BU9erVPR1Cge655x5Ph1Cg7t27ezoEAABQQnHnAgAAAIApSC4AAAAAmILkAgAAAIApSC4AAAAAmILkAgAAAIApSC4AAAAAmILkAgAAAIApSC4AAAAAmILkAgAAAIApSC4AAAAAmILkAgAAAIApSC4AAAAAmILkAgAAAIApSC4AAAAAmILkAgAAAIApXEouIiMjZbFY8i0JCQlFFR8AAACAEqKUK5U3bdqk3Nxc+/r27dt1xx136IEHHjA9MAAAAAAli0vJRWBgoMP6+PHjVbNmTbVt27bAbbKzs5WdnW1fz8zMdDFEAAAAACVBocdc5OTk6KOPPtKAAQNksVgKrJecnKyAgAD7EhERUdgmAQAAABRjhU4uFi1apNOnT6tfv36XrZeYmKiMjAz7cvjw4cI2CQAAzGCxuGcBcMNxqVvUxd577z116dJFYWFhl61ntVpltVoL2wwAAACKwIEJd7mnofGGe9pBsVCo5OLgwYNasWKFPv/8c7PjAQAAAFBCFapb1OzZsxUUFKSuXbuaHQ8AAACAEsrlOxc2m02zZ89WfHy8SpUqdK8qAACAG5u7xqUYdEuC+7h852LFihU6dOiQBgwYUBTxAAAAACihXL710KlTJxlkwAAAAAAuUeipaAEAAADgYiQXAAAAAExBcgEAAADAFCQXAAAAAExBcgEAAADAFCQXAAAAAEzBU/AAAMCNhwfYAUWC5AIAAACeQZJ33aFbFACgUKZOnarIyEj5+voqJiZGGzduvGz9BQsWqF69evL19VXjxo317bffuilSFDsWi3sWAG5HcgEAcNn8+fM1atQoJSUlacuWLWratKni4uKUnp7utP66devUu3dvDRw4UD/99JO6d++u7t27a/v27W6OHABQlEguAAAumzRpkgYPHqz+/furQYMGmj59usqUKaNZs2Y5rf/GG2+oc+fOeuqpp1S/fn29+OKLat68uaZMmeLmyAEARcntYy6M/9/nLTMz091Nl2hnzpzxdAgFunDhgqdDKHHOnz/v6RAKVFz/b3p5Fb/fQvKOlXGD9eXNyclRSkqKEhMT7WVeXl7q2LGj1q9f73Sb9evXa9SoUQ5lcXFxWrRoUYHtZGdnKzs7276ekZEhqfj+jTooCTG6g6ePg6fbJ4bi0X5xiaEEc+V65/bkIu9LckREhLubBoqN4tzXvFq1ap4OocQ5c+aMAgICPB2G25w8eVK5ubkKDg52KA8ODtauXbucbpOWlua0flpaWoHtJCcna+zYsfnKS8T14wb6e7gsTx8HT7dPDMWj/eISw3Xgaq53bk8uwsLCdPjwYZUrV06WaxxslZmZqYiICB0+fFjly5c3KcLrG8fMdRwz190ox8wwDJ05c0ZhYWGeDuW6lJiY6HC3w2az6dSpU6pcufI1Xz+uVnH4WyYGz7dPDMWjfWLwXPuuXO/cnlx4eXnppptuMnWf5cuXv66/wBQFjpnrOGauuxGO2Y10xyJPlSpV5O3trePHjzuUHz9+XCEhIU63CQkJcam+JFmtVlmtVoeyChUqFC7oa1Qc/paJwfPtE0PxaJ8YPNP+1V7vil8nZgBAsebj46MWLVpo5cqV9jKbzaaVK1cqNjbW6TaxsbEO9SVp+fLlBdYHAJRMPEQPAOCyUaNGKT4+XtHR0WrZsqUmT56srKws9e/fX5LUt29fhYeHKzk5WZI0fPhwtW3bVhMnTlTXrl01b948bd68WTNmzPDk2wAAmKxEJxdWq1VJSUn5bpujYBwz13HMXMcxu/716tVLJ06c0OjRo5WWlqaoqCgtXbrUPmj70KFDDjN8tWrVSnPnztW//vUvPfvss6pdu7YWLVqkRo0aeeotXJXi8LdMDJ5vnxiKR/vEUDzavxKLcaPNoQgAAACgSDDmAgAAAIApSC4AAAAAmILkAgAAAIApSC4AAAAAmKLEJhdTp05VZGSkfH19FRMTo40bN3o6pGIrOTlZN998s8qVK6egoCB1795du3fv9nRYJcr48eNlsVg0YsQIT4dSrB05ckQPP/ywKleuLD8/PzVu3FibN2/2dFhAoXnyWvPDDz+oW7duCgsLk8Vi0aJFi9zWtlQ8rh3Tpk1TkyZN7A8Li42N1ZIlS9waw8U8cS0YM2aMLBaLw1KvXj23tZ/H0+f3yMjIfMfBYrEoISHBLe3n5ubq+eefV/Xq1eXn56eaNWvqxRdflLvnRTpz5oxGjBihatWqyc/PT61atdKmTZvcGsOVlMjkYv78+Ro1apSSkpK0ZcsWNW3aVHFxcUpPT/d0aMXSmjVrlJCQoA0bNmj58uU6f/68OnXqpKysLE+HViJs2rRJ77zzjpo0aeLpUIq1P/74Q61bt1bp0qW1ZMkS7dixQxMnTlTFihU9HRpQKJ6+1mRlZalp06aaOnWqW9q7VHG4dtx0000aP368UlJStHnzZrVv31733HOPfvnlF7fFkMeT14KGDRvq2LFj9mXt2rVubb84nN83bdrkcAyWL18uSXrggQfc0v6ECRM0bdo0TZkyRTt37tSECRP0yiuv6K233nJL+3kGDRqk5cuX68MPP9S2bdvUqVMndezYUUeOHHFrHJdllEAtW7Y0EhIS7Ou5ublGWFiYkZyc7MGoSo709HRDkrFmzRpPh1LsnTlzxqhdu7axfPlyo23btsbw4cM9HVKx9fTTTxu33nqrp8MATFOcrjWSjC+++MLt7V6suFw7KlasaLz77rtubdOT14KkpCSjadOmbmvPmeJ4fh8+fLhRs2ZNw2azuaW9rl27GgMGDHAou++++4w+ffq4pX3DMIxz584Z3t7exjfffONQ3rx5c+O5555zWxxXUuLuXOTk5CglJUUdO3a0l3l5ealjx45av369ByMrOTIyMiRJlSpV8nAkxV9CQoK6du3q8PcG57766itFR0frgQceUFBQkJo1a6aZM2d6OiygULjW5Ofpa0dubq7mzZunrKwsxcbGurVtT18Lfv31V4WFhalGjRrq06ePDh065Nb2i9v5PScnRx999JEGDBggi8XiljZbtWqllStXas+ePZKkrVu3au3aterSpYtb2pekCxcuKDc3V76+vg7lfn5+br+bdTkl7gndJ0+eVG5urv0psHmCg4O1a9cuD0VVcthsNo0YMUKtW7cu9k/G9bR58+Zpy5Ytxa4vY3G1b98+TZs2TaNGjdKzzz6rTZs2adiwYfLx8VF8fLynwwNcwrXGkSevHdu2bVNsbKz++usvlS1bVl988YUaNGjgtvY9fS2IiYnRnDlzVLduXR07dkxjx45VmzZttH37dpUrV84tMRS38/uiRYt0+vRp9evXz21tPvPMM8rMzFS9evXk7e2t3Nxcvfzyy+rTp4/bYihXrpxiY2P14osvqn79+goODtYnn3yi9evXq1atWm6L40pKXHKBa5OQkKDt27cXqwy3ODp8+LCGDx+u5cuX5/uFAM7ZbDZFR0dr3LhxkqRmzZpp+/btmj59OskFUMJ58tpRt25dpaamKiMjQwsXLlR8fLzWrFnjlgSjOFwLLv5lvEmTJoqJiVG1atX06aefauDAgW6Jobid39977z116dJFYWFhbmvz008/1ccff6y5c+eqYcOGSk1N1YgRIxQWFubWY/Dhhx9qwIABCg8Pl7e3t5o3b67evXsrJSXFbTFcSYnrFlWlShV5e3vr+PHjDuXHjx9XSEiIh6IqGYYOHapvvvlGq1at0k033eTpcIq1lJQUpaenq3nz5ipVqpRKlSqlNWvW6M0331SpUqWUm5vr6RCLndDQ0HwX+/r167v99j1gBq41/+Ppa4ePj49q1aqlFi1aKDk5WU2bNtUbb7zhlraL47WgQoUKqlOnjvbu3eu2NovT+f3gwYNasWKFBg0a5NZ2n3rqKT3zzDN68MEH1bhxYz3yyCMaOXKkkpOT3RpHzZo1tWbNGp09e1aHDx/Wxo0bdf78edWoUcOtcVxOiUsufHx81KJFC61cudJeZrPZtHLlSrf3wSwpDMPQ0KFD9cUXX+j7779X9erVPR1SsdehQwdt27ZNqamp9iU6Olp9+vRRamqqvL29PR1isdO6det801Tu2bNH1apV81BEQOFxrSm+1w6bzabs7Gy3tFUcrwVnz57Vb7/9ptDQULe1WZzO77Nnz1ZQUJC6du3q1nbPnTsnLy/Hr83e3t6y2WxujSOPv7+/QkND9ccff2jZsmW65557PBKHMyWyW9SoUaMUHx+v6OhotWzZUpMnT1ZWVpb69+/v6dCKpYSEBM2dO1dffvmlypUrp7S0NElSQECA/Pz8PBxd8VSuXLl8/Yr9/f1VuXJlxqoUYOTIkWrVqpXGjRunnj17auPGjZoxY4ZmzJjh6dCAQvH0tebs2bMOv07v379fqampqlSpkqpWrVrk7ReHa0diYqK6dOmiqlWr6syZM5o7d65Wr16tZcuWuaX94nAtePLJJ9WtWzdVq1ZNR48eVVJSkry9vdW7d2+3tC8Vn/O7zWbT7NmzFR8fr1Kl3PsVtlu3bnr55ZdVtWpVNWzYUD/99JMmTZqkAQMGuDWOZcuWyTAM1a1bV3v37tVTTz2levXqFa/vwB6erarQ3nrrLaNq1aqGj4+P0bJlS2PDhg2eDqnYkuR0mT17tqdDK1GYivbKvv76a6NRo0aG1Wo16tWrZ8yYMcPTIQHXxJPXmlWrVjk9d8fHx7ul/eJw7RgwYIBRrVo1w8fHxwgMDDQ6dOhgfPfdd25r3xl3Xwt69eplhIaGGj4+PkZ4eLjRq1cvY+/evW5rP09xOL8vW7bMkGTs3r3b7W1nZmYaw4cPN6pWrWr4+voaNWrUMJ577jkjOzvbrXHMnz/fqFGjhuHj42OEhIQYCQkJxunTp90aw5VYDMPNjxYEAAAAcF0qcWMuAAAAABRPJBcAAAAATEFyAQAAAMAUJBcAAAAATEFyAQAAAMAUJBcAAAAATEFyAQAAAMAUJBcAAAAATEFyAQAA8P9ZLBYtWrToquuvXr1aFotFp0+fNjWOyMhITZ482dR9Au5AcgEAAK5r/fr1k8VikcViUenSpRUcHKw77rhDs2bNks1mc6h77NgxdenS5ar33apVKx07dkwBAQGSpDlz5qhChQpmhg+UKCQXAADgute5c2cdO3ZMBw4c0JIlS3T77bdr+PDhuuuuu3ThwgV7vZCQEFmt1qver4+Pj0JCQmSxWIoibKDEIbkAAADXPavVqpCQEIWHh6t58+Z69tln9eWXX2rJkiWaM2eOvd6l3aLWrVunqKgo+fr6Kjo6WosWLZLFYlFqaqokx25Rq1evVv/+/ZWRkWG/UzJmzJgCY/r666918803y9fXV1WqVNG9995bYN1JkyapcePG8vf3V0REhJ544gmdPXvW/vrBgwfVrVs3VaxYUf7+/mrYsKG+/fZbSdIff/yhPn36KDAwUH5+fqpdu7Zmz55dqOMIXEkpTwcAAADgCe3bt1fTpk31+eefa9CgQflez8zMVLdu3XTnnXdq7ty5OnjwoEaMGFHg/lq1aqXJkydr9OjR2r17tySpbNmyTusuXrxY9957r5577jl98MEHysnJsScDznh5eenNN99U9erVtW/fPj3xxBP65z//qbfffluSlJCQoJycHP3www/y9/fXjh077G0///zz2rFjh5YsWaIqVapo7969+vPPP6/2MAEuIbkAAAA3rHr16unnn392+trcuXNlsVg0c+ZM+fr6qkGDBjpy5IgGDx7stL6Pj48CAgJksVgUEhJy2XZffvllPfjggxo7dqy9rGnTpgXWvzipiYyM1EsvvaQhQ4bYk4tDhw7p/vvvV+PGjSVJNWrUsNc/dOiQmjVrpujoaPv2QFGhWxQAALhhGYZR4HiJ3bt3q0mTJvL19bWXtWzZ0pR2U1NT1aFDh6uuv2LFCnXo0EHh4eEqV66cHnnkEf3+++86d+6cJGnYsGF66aWX1Lp1ayUlJTkkTI8//rjmzZunqKgo/fOf/9S6detMeQ+AMyQXAADghrVz505Vr17d7e36+flddd0DBw7orrvuUpMmTfTZZ58pJSVFU6dOlSTl5ORIkgYNGqR9+/bpkUce0bZt2xQdHa233npLktSlSxcdPHhQI0eO1NGjR9WhQwc9+eST5r8pQCQXAADgBvX9999r27Ztuv/++52+XrduXW3btk3Z2dn2sk2bNl12nz4+PsrNzb1i202aNNHKlSuvKs6UlBTZbDZNnDhRt9xyi+rUqaOjR4/mqxcREaEhQ4bo888/1//93/9p5syZ9tcCAwMVHx+vjz76SJMnT9aMGTOuqm3AVSQXAADgupedna20tDQdOXJEW7Zs0bhx43TPPfforrvuUt++fZ1u89BDD8lms+nRRx/Vzp07tWzZMr322muSVGBXqsjISJ09e1YrV67UyZMn7d2WLpWUlKRPPvlESUlJ2rlzp7Zt26YJEyY4rVurVi2dP39eb731lvbt26cPP/xQ06dPd6gzYsQILVu2TPv379eWLVu0atUq1a9fX5I0evRoffnll9q7d69++eUXffPNN/bXALORXAAAgOve0qVLFRoaqsjISHXu3FmrVq3Sm2++qS+//FLe3t5Otylfvry+/vprpaamKioqSs8995xGjx4tSQ7jMC7WqlUrDRkyRL169VJgYKBeeeUVp/XatWunBQsW6KuvvlJUVJTat2+vjRs3Oq3btGlTTZo0SRMmTFCjRo308ccfKzk52aFObm6uEhISVL9+fXXu3Fl16tSxD/b28fFRYmKimjRpottuu03e3t6aN2/eVR03wFUWwzAMTwcBAABQEnz88cf2Z1m4Mm4CuFEwFS0AAEABPvjgA9WoUUPh4eHaunWrnn76afXs2ZPEAigAyQUAAEAB0tLSNHr0aKWlpSk0NFQPPPCAXn75ZU+HBRRbdIsCAAAAYAoGdAMAAAAwBckFAAAAAFOQXAAAAAAwBckFAAAAAFOQXAAAAAAwBckFAAAAAFOQXAAAAAAwBckFAAAAAFP8P/9Jx16eWUn4AAAAAElFTkSuQmCC",
+      "text/plain": [
+       "<Figure size 1000x400 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 1000x400 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 1000x400 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 1000x400 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 1000x400 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 1000x400 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
-    "# Your code here"
+    "\n",
+    "\n",
+    "# Get predictions on test set\n",
+    "test_predictions = model.forward(X_test)\n",
+    "predicted_classes = np.argmax(test_predictions, axis=1)\n",
+    "true_classes = y_test\n",
+    "\n",
+    "# Calculate loss for each test sample individually\n",
+    "sample_losses = []\n",
+    "for i in range(len(X_test)):\n",
+    "    # Get single sample\n",
+    "    x_sample = X_test[i:i+1]\n",
+    "    y_sample = np.array([y_test[i]])\n",
+    "    \n",
+    "    # Calculate loss for this sample\n",
+    "    loss = model.loss(x_sample, y_sample)\n",
+    "    sample_losses.append(loss)\n",
+    "\n",
+    "sample_losses = np.array(sample_losses)\n",
+    "\n",
+    "# Find indices of worst predictions (highest loss)\n",
+    "# Get indices sorted by loss (highest first)\n",
+    "worst_indices = np.argsort(sample_losses)[-9:][::-1]  # Top 9 worst\n",
+    "\n",
+    "print(f\"\\nTop 9 worst predictions (highest loss):\")\n",
+    "print(\"-\" * 60)\n",
+    "print(f\"{'Rank':<6} {'Sample':<8} {'True':<6} {'Pred':<6} {'Loss':<10} {'Confidence'}\")\n",
+    "print(\"-\" * 60)\n",
+    "\n",
+    "for rank, idx in enumerate(worst_indices):\n",
+    "    true = true_classes[idx]\n",
+    "    pred = predicted_classes[idx]\n",
+    "    loss = sample_losses[idx]\n",
+    "    confidence = np.max(test_predictions[idx])\n",
+    "    print(f\"{rank+1:<6} {idx:<8} {true:<6} {pred:<6} {loss:<10.4f} {confidence:.2%}\")\n",
+    "\n",
+    "print(\"-\" * 60)\n",
+    "\n",
+    "print(\"\\n\" + \"=\" * 70)\n",
+    "print(\"DISPLAY WORST PREDICTIONS\")\n",
+    "print(\"=\" * 70)\n",
+    "\n",
+    "for i, idx in enumerate(worst_indices):\n",
+    "    print(f\"\\n--- Worst Prediction #{i+1} (Sample {idx}) ---\")\n",
+    "    print(f\"True class: {true_classes[idx]}, Predicted: {predicted_classes[idx]}\")\n",
+    "    print(f\"Loss: {sample_losses[idx]:.4f}\")\n",
+    "    plot_prediction(model, sample_idx=idx)"
    ]
   },
   {
@@ -803,15 +1557,247 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 56,
    "metadata": {
     "collapsed": false
    },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "1. DIFFERENT LEARNING RATES\n",
+      "------------------------------\n",
+      "\n",
+      "Learning rate = 0.1\n",
+      "✓ NeuralNet initialized:\n",
+      "  - Input size: 64\n",
+      "  - Hidden size: 10\n",
+      "  - Output size: 10\n",
+      "  - W_h shape: (64, 10)\n",
+      "  - b_h shape: (10,)\n",
+      "  - W_o shape: (10, 10)\n",
+      "  - b_o shape: (10,)\n",
+      "  Train accuracy: 0.100\n",
+      "  Test accuracy:  0.107\n",
+      "\n",
+      "Learning rate = 0.01\n",
+      "✓ NeuralNet initialized:\n",
+      "  - Input size: 64\n",
+      "  - Hidden size: 10\n",
+      "  - Output size: 10\n",
+      "  - W_h shape: (64, 10)\n",
+      "  - b_h shape: (10,)\n",
+      "  - W_o shape: (10, 10)\n",
+      "  - b_o shape: (10,)\n",
+      "  Train accuracy: 0.109\n",
+      "  Test accuracy:  0.063\n",
+      "\n",
+      "Learning rate = 0.001\n",
+      "✓ NeuralNet initialized:\n",
+      "  - Input size: 64\n",
+      "  - Hidden size: 10\n",
+      "  - Output size: 10\n",
+      "  - W_h shape: (64, 10)\n",
+      "  - b_h shape: (10,)\n",
+      "  - W_o shape: (10, 10)\n",
+      "  - b_o shape: (10,)\n",
+      "  Train accuracy: 0.930\n",
+      "  Test accuracy:  0.893\n",
+      "\n",
+      "2. DIFFERENT HIDDEN LAYER SIZES\n",
+      "------------------------------\n",
+      "\n",
+      "Hidden size = 5\n",
+      "✓ NeuralNet initialized:\n",
+      "  - Input size: 64\n",
+      "  - Hidden size: 5\n",
+      "  - Output size: 10\n",
+      "  - W_h shape: (64, 5)\n",
+      "  - b_h shape: (5,)\n",
+      "  - W_o shape: (5, 10)\n",
+      "  - b_o shape: (10,)\n",
+      "  Train accuracy: 0.109\n",
+      "  Test accuracy:  0.063\n",
+      "\n",
+      "Hidden size = 10\n",
+      "✓ NeuralNet initialized:\n",
+      "  - Input size: 64\n",
+      "  - Hidden size: 10\n",
+      "  - Output size: 10\n",
+      "  - W_h shape: (64, 10)\n",
+      "  - b_h shape: (10,)\n",
+      "  - W_o shape: (10, 10)\n",
+      "  - b_o shape: (10,)\n",
+      "  Train accuracy: 0.109\n",
+      "  Test accuracy:  0.063\n",
+      "\n",
+      "Hidden size = 20\n",
+      "✓ NeuralNet initialized:\n",
+      "  - Input size: 64\n",
+      "  - Hidden size: 20\n",
+      "  - Output size: 10\n",
+      "  - W_h shape: (64, 20)\n",
+      "  - b_h shape: (20,)\n",
+      "  - W_o shape: (20, 10)\n",
+      "  - b_o shape: (10,)\n",
+      "  Train accuracy: 0.145\n",
+      "  Test accuracy:  0.167\n",
+      "\n",
+      "--- ADDING SECOND HIDDEN LAYER ---\n",
+      "2-layer test accuracy: 0.178\n"
+     ]
+    }
+   ],
+   "source": [
+    "\n",
+    "\n",
+    "print(\"\\n1. DIFFERENT LEARNING RATES\")\n",
+    "print(\"-\" * 30)\n",
+    "\n",
+    "learning_rates = [0.1, 0.01, 0.001]\n",
+    "\n",
+    "for lr in learning_rates:\n",
+    "    print(f\"\\nLearning rate = {lr}\")\n",
+    "    model = NeuralNet(n_features, hidden_size=10, output_size=n_classes)\n",
+    "    \n",
+    "    # Train for a few epochs\n",
+    "    for epoch in range(5):\n",
+    "        for x, y in zip(X_train, y_train):\n",
+    "            model.train(x, y, lr)\n",
+    "    \n",
+    "    train_acc = model.accuracy(X_train, y_train)\n",
+    "    test_acc = model.accuracy(X_test, y_test)\n",
+    "    print(f\"  Train accuracy: {train_acc:.3f}\")\n",
+    "    print(f\"  Test accuracy:  {test_acc:.3f}\")\n",
+    "\n",
+    "\n",
+    "print(\"\\n2. DIFFERENT HIDDEN LAYER SIZES\")\n",
+    "print(\"-\" * 30)\n",
+    "\n",
+    "hidden_sizes = [5, 10, 20]\n",
+    "\n",
+    "for h in hidden_sizes:\n",
+    "    print(f\"\\nHidden size = {h}\")\n",
+    "    model = NeuralNet(n_features, hidden_size=h, output_size=n_classes)\n",
+    "    \n",
+    "    for epoch in range(5):\n",
+    "        for x, y in zip(X_train, y_train):\n",
+    "            model.train(x, y, learning_rate=0.01)\n",
+    "    \n",
+    "    train_acc = model.accuracy(X_train, y_train)\n",
+    "    test_acc = model.accuracy(X_test, y_test)\n",
+    "    print(f\"  Train accuracy: {train_acc:.3f}\")\n",
+    "    print(f\"  Test accuracy:  {test_acc:.3f}\")\n",
+    "\n",
+    "\n",
+    "print(\"\\n--- ADDING SECOND HIDDEN LAYER ---\")\n",
+    "\n",
+    "# Simple 2-hidden-layer model (just modify the existing class)\n",
+    "class NeuralNet2Layer(NeuralNet):\n",
+    "    def __init__(self, input_size, hidden_size1, hidden_size2, output_size):\n",
+    "        # Initialize first hidden layer (same as before)\n",
+    "        self.W_h1 = np.random.uniform(size=(input_size, hidden_size1), high=0.1, low=-0.1)\n",
+    "        self.b_h1 = np.random.uniform(size=hidden_size1, high=0.1, low=-0.1)\n",
+    "        \n",
+    "        # Second hidden layer\n",
+    "        self.W_h2 = np.random.uniform(size=(hidden_size1, hidden_size2), high=0.1, low=-0.1)\n",
+    "        self.b_h2 = np.random.uniform(size=hidden_size2, high=0.1, low=-0.1)\n",
+    "        \n",
+    "        # Output layer\n",
+    "        self.W_o = np.random.uniform(size=(hidden_size2, output_size), high=0.1, low=-0.1)\n",
+    "        self.b_o = np.random.uniform(size=output_size, high=0.1, low=-0.1)\n",
+    "        \n",
+    "        self.input_size = input_size\n",
+    "        self.hidden_size2 = hidden_size2\n",
+    "        self.output_size = output_size\n",
+    "    \n",
+    "    def forward(self, X):\n",
+    "        # First hidden layer\n",
+    "        Z_h1 = np.dot(X, self.W_h1) + self.b_h1\n",
+    "        H1 = sigmoid(Z_h1)\n",
+    "        \n",
+    "        # Second hidden layer\n",
+    "        Z_h2 = np.dot(H1, self.W_h2) + self.b_h2\n",
+    "        H2 = sigmoid(Z_h2)\n",
+    "        \n",
+    "        # Output layer\n",
+    "        Z_o = np.dot(H2, self.W_o) + self.b_o\n",
+    "        return softmax(Z_o)\n",
+    "    \n",
+    "    def train(self, x, y, lr):\n",
+    "        x = x[np.newaxis, :]\n",
+    "        y_onehot = one_hot(self.output_size, np.array([y]))\n",
+    "        \n",
+    "        # Forward pass (store intermediate values for backprop)\n",
+    "        Z_h1 = np.dot(x, self.W_h1) + self.b_h1\n",
+    "        H1 = sigmoid(Z_h1)\n",
+    "        Z_h2 = np.dot(H1, self.W_h2) + self.b_h2\n",
+    "        H2 = sigmoid(Z_h2)\n",
+    "        Z_o = np.dot(H2, self.W_o) + self.b_o\n",
+    "        y_pred = softmax(Z_o)\n",
+    "        \n",
+    "        # Backward pass\n",
+    "        error_o = y_pred - y_onehot\n",
+    "        error_h2 = np.dot(error_o, self.W_o.T) * dsigmoid(Z_h2)\n",
+    "        error_h1 = np.dot(error_h2, self.W_h2.T) * dsigmoid(Z_h1)\n",
+    "        \n",
+    "        # Update weights\n",
+    "        self.W_o -= lr * np.dot(H2.T, error_o)\n",
+    "        self.b_o -= lr * np.sum(error_o, axis=0)\n",
+    "        self.W_h2 -= lr * np.dot(H1.T, error_h2)\n",
+    "        self.b_h2 -= lr * np.sum(error_h2, axis=0)\n",
+    "        self.W_h1 -= lr * np.dot(x.T, error_h1)\n",
+    "        self.b_h1 -= lr * np.sum(error_h1, axis=0)\n",
+    "\n",
+    "model2 = NeuralNet2Layer(n_features, hidden_size1=10, hidden_size2=5, output_size=n_classes)\n",
+    "\n",
+    "for epoch in range(5):\n",
+    "    for x, y in zip(X_train, y_train):\n",
+    "        model2.train(x, y, 0.01)\n",
+    "\n",
+    "test_acc = model2.accuracy(X_test, y_test)\n",
+    "print(f\"2-layer test accuracy: {test_acc:.3f}\")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Best test accuracy: 89.3% with:\n",
+    "\n",
+    "Learning rate: 0.001\n",
+    "\n",
+    "Hidden layer size: 10\n",
+    "\n",
+    "Number of hidden layers: 1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
    "outputs": [],
    "source": [
-    "# Your code here"
+    "\"\"\"Best test accuracy: 89.3% with:\n",
+    "\n",
+    "Learning rate: 0.001\n",
+    "\n",
+    "Hidden layer size: 10\n",
+    "\n",
+    "Number of hidden layers: \n",
+    "\n",
+    "With more time, I would train at .001 learning rate.\"\"\""
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -822,21 +1808,9 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": ".venv",
+   "display_name": "Deep Learning (.venv)",
    "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.12.12"
+   "name": "deeplearning_venv"
   }
  },
  "nbformat": 4,
diff --git a/01_materials/labs/lab_3.ipynb b/01_materials/labs/lab_3.ipynb
index ef6d808c7..07f9a4f75 100644
--- a/01_materials/labs/lab_3.ipynb
+++ b/01_materials/labs/lab_3.ipynb
@@ -15,9 +15,32 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2026-02-17 02:22:44.443589: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n",
+      "To enable the following instructions: AVX2 AVX512F FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "GPUs disabled, using CPU.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2026-02-17 02:25:00.246049: E external/local_xla/xla/stream_executor/cuda/cuda_platform.cc:51] failed call to cuInit: INTERNAL: CUDA error: Failed call to cuInit: CUDA_ERROR_NO_DEVICE: no CUDA-capable device is detected\n"
+     ]
+    }
+   ],
    "source": [
     "## Tensorflow GPU disabled by default. Comment the following lines to enable GPU if you have a working CUDA installation.\n",
     "import tensorflow as tf\n",
@@ -38,7 +61,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -75,9 +98,141 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>user_id</th>\n",
+       "      <th>item_id</th>\n",
+       "      <th>rating</th>\n",
+       "      <th>timestamp</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>196</td>\n",
+       "      <td>242</td>\n",
+       "      <td>3</td>\n",
+       "      <td>881250949</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>186</td>\n",
+       "      <td>302</td>\n",
+       "      <td>3</td>\n",
+       "      <td>891717742</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>22</td>\n",
+       "      <td>377</td>\n",
+       "      <td>1</td>\n",
+       "      <td>878887116</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>244</td>\n",
+       "      <td>51</td>\n",
+       "      <td>2</td>\n",
+       "      <td>880606923</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>166</td>\n",
+       "      <td>346</td>\n",
+       "      <td>1</td>\n",
+       "      <td>886397596</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>99995</th>\n",
+       "      <td>880</td>\n",
+       "      <td>476</td>\n",
+       "      <td>3</td>\n",
+       "      <td>880175444</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>99996</th>\n",
+       "      <td>716</td>\n",
+       "      <td>204</td>\n",
+       "      <td>5</td>\n",
+       "      <td>879795543</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>99997</th>\n",
+       "      <td>276</td>\n",
+       "      <td>1090</td>\n",
+       "      <td>1</td>\n",
+       "      <td>874795795</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>99998</th>\n",
+       "      <td>13</td>\n",
+       "      <td>225</td>\n",
+       "      <td>2</td>\n",
+       "      <td>882399156</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>99999</th>\n",
+       "      <td>12</td>\n",
+       "      <td>203</td>\n",
+       "      <td>3</td>\n",
+       "      <td>879959583</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>100000 rows × 4 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       user_id  item_id  rating  timestamp\n",
+       "0          196      242       3  881250949\n",
+       "1          186      302       3  891717742\n",
+       "2           22      377       1  878887116\n",
+       "3          244       51       2  880606923\n",
+       "4          166      346       1  886397596\n",
+       "...        ...      ...     ...        ...\n",
+       "99995      880      476       3  880175444\n",
+       "99996      716      204       5  879795543\n",
+       "99997      276     1090       1  874795795\n",
+       "99998       13      225       2  882399156\n",
+       "99999       12      203       3  879959583\n",
+       "\n",
+       "[100000 rows x 4 columns]"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "import pandas as pd\n",
     "\n",
@@ -99,9 +254,166 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 4,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>item_id</th>\n",
+       "      <th>title</th>\n",
+       "      <th>release_date</th>\n",
+       "      <th>video_release_date</th>\n",
+       "      <th>imdb_url</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>Toy Story (1995)</td>\n",
+       "      <td>01-Jan-1995</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>http://us.imdb.com/M/title-exact?Toy%20Story%2...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2</td>\n",
+       "      <td>GoldenEye (1995)</td>\n",
+       "      <td>01-Jan-1995</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>http://us.imdb.com/M/title-exact?GoldenEye%20(...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>3</td>\n",
+       "      <td>Four Rooms (1995)</td>\n",
+       "      <td>01-Jan-1995</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>http://us.imdb.com/M/title-exact?Four%20Rooms%...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>4</td>\n",
+       "      <td>Get Shorty (1995)</td>\n",
+       "      <td>01-Jan-1995</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>http://us.imdb.com/M/title-exact?Get%20Shorty%...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>5</td>\n",
+       "      <td>Copycat (1995)</td>\n",
+       "      <td>01-Jan-1995</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>http://us.imdb.com/M/title-exact?Copycat%20(1995)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1677</th>\n",
+       "      <td>1678</td>\n",
+       "      <td>Mat' i syn (1997)</td>\n",
+       "      <td>06-Feb-1998</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>http://us.imdb.com/M/title-exact?Mat%27+i+syn+...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1678</th>\n",
+       "      <td>1679</td>\n",
+       "      <td>B. Monkey (1998)</td>\n",
+       "      <td>06-Feb-1998</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>http://us.imdb.com/M/title-exact?B%2E+Monkey+(...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1679</th>\n",
+       "      <td>1680</td>\n",
+       "      <td>Sliding Doors (1998)</td>\n",
+       "      <td>01-Jan-1998</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>http://us.imdb.com/Title?Sliding+Doors+(1998)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1680</th>\n",
+       "      <td>1681</td>\n",
+       "      <td>You So Crazy (1994)</td>\n",
+       "      <td>01-Jan-1994</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>http://us.imdb.com/M/title-exact?You%20So%20Cr...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1681</th>\n",
+       "      <td>1682</td>\n",
+       "      <td>Scream of Stone (Schrei aus Stein) (1991)</td>\n",
+       "      <td>08-Mar-1996</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>http://us.imdb.com/M/title-exact?Schrei%20aus%...</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>1682 rows × 5 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      item_id                                      title release_date  \\\n",
+       "0           1                           Toy Story (1995)  01-Jan-1995   \n",
+       "1           2                           GoldenEye (1995)  01-Jan-1995   \n",
+       "2           3                          Four Rooms (1995)  01-Jan-1995   \n",
+       "3           4                          Get Shorty (1995)  01-Jan-1995   \n",
+       "4           5                             Copycat (1995)  01-Jan-1995   \n",
+       "...       ...                                        ...          ...   \n",
+       "1677     1678                          Mat' i syn (1997)  06-Feb-1998   \n",
+       "1678     1679                           B. Monkey (1998)  06-Feb-1998   \n",
+       "1679     1680                       Sliding Doors (1998)  01-Jan-1998   \n",
+       "1680     1681                        You So Crazy (1994)  01-Jan-1994   \n",
+       "1681     1682  Scream of Stone (Schrei aus Stein) (1991)  08-Mar-1996   \n",
+       "\n",
+       "      video_release_date                                           imdb_url  \n",
+       "0                    NaN  http://us.imdb.com/M/title-exact?Toy%20Story%2...  \n",
+       "1                    NaN  http://us.imdb.com/M/title-exact?GoldenEye%20(...  \n",
+       "2                    NaN  http://us.imdb.com/M/title-exact?Four%20Rooms%...  \n",
+       "3                    NaN  http://us.imdb.com/M/title-exact?Get%20Shorty%...  \n",
+       "4                    NaN  http://us.imdb.com/M/title-exact?Copycat%20(1995)  \n",
+       "...                  ...                                                ...  \n",
+       "1677                 NaN  http://us.imdb.com/M/title-exact?Mat%27+i+syn+...  \n",
+       "1678                 NaN  http://us.imdb.com/M/title-exact?B%2E+Monkey+(...  \n",
+       "1679                 NaN      http://us.imdb.com/Title?Sliding+Doors+(1998)  \n",
+       "1680                 NaN  http://us.imdb.com/M/title-exact?You%20So%20Cr...  \n",
+       "1681                 NaN  http://us.imdb.com/M/title-exact?Schrei%20aus%...  \n",
+       "\n",
+       "[1682 rows x 5 columns]"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "columns_to_keep = ['item_id', 'title', 'release_date', 'video_release_date', 'imdb_url']\n",
     "items = pd.read_csv(ML_100K_FOLDER / \"u.item\", sep='|', names=columns_to_keep,\n",
@@ -120,7 +432,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -137,7 +449,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -146,9 +458,134 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 7,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>item_id</th>\n",
+       "      <th>title</th>\n",
+       "      <th>release_date</th>\n",
+       "      <th>video_release_date</th>\n",
+       "      <th>imdb_url</th>\n",
+       "      <th>release_year</th>\n",
+       "      <th>user_id</th>\n",
+       "      <th>rating</th>\n",
+       "      <th>timestamp</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>Toy Story (1995)</td>\n",
+       "      <td>1995-01-01</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>http://us.imdb.com/M/title-exact?Toy%20Story%2...</td>\n",
+       "      <td>1995.0</td>\n",
+       "      <td>308</td>\n",
+       "      <td>4</td>\n",
+       "      <td>887736532</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1</td>\n",
+       "      <td>Toy Story (1995)</td>\n",
+       "      <td>1995-01-01</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>http://us.imdb.com/M/title-exact?Toy%20Story%2...</td>\n",
+       "      <td>1995.0</td>\n",
+       "      <td>287</td>\n",
+       "      <td>5</td>\n",
+       "      <td>875334088</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>1</td>\n",
+       "      <td>Toy Story (1995)</td>\n",
+       "      <td>1995-01-01</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>http://us.imdb.com/M/title-exact?Toy%20Story%2...</td>\n",
+       "      <td>1995.0</td>\n",
+       "      <td>148</td>\n",
+       "      <td>4</td>\n",
+       "      <td>877019411</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>1</td>\n",
+       "      <td>Toy Story (1995)</td>\n",
+       "      <td>1995-01-01</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>http://us.imdb.com/M/title-exact?Toy%20Story%2...</td>\n",
+       "      <td>1995.0</td>\n",
+       "      <td>280</td>\n",
+       "      <td>4</td>\n",
+       "      <td>891700426</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>1</td>\n",
+       "      <td>Toy Story (1995)</td>\n",
+       "      <td>1995-01-01</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>http://us.imdb.com/M/title-exact?Toy%20Story%2...</td>\n",
+       "      <td>1995.0</td>\n",
+       "      <td>66</td>\n",
+       "      <td>3</td>\n",
+       "      <td>883601324</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   item_id             title release_date  video_release_date  \\\n",
+       "0        1  Toy Story (1995)   1995-01-01                 NaN   \n",
+       "1        1  Toy Story (1995)   1995-01-01                 NaN   \n",
+       "2        1  Toy Story (1995)   1995-01-01                 NaN   \n",
+       "3        1  Toy Story (1995)   1995-01-01                 NaN   \n",
+       "4        1  Toy Story (1995)   1995-01-01                 NaN   \n",
+       "\n",
+       "                                            imdb_url  release_year  user_id  \\\n",
+       "0  http://us.imdb.com/M/title-exact?Toy%20Story%2...        1995.0      308   \n",
+       "1  http://us.imdb.com/M/title-exact?Toy%20Story%2...        1995.0      287   \n",
+       "2  http://us.imdb.com/M/title-exact?Toy%20Story%2...        1995.0      148   \n",
+       "3  http://us.imdb.com/M/title-exact?Toy%20Story%2...        1995.0      280   \n",
+       "4  http://us.imdb.com/M/title-exact?Toy%20Story%2...        1995.0       66   \n",
+       "\n",
+       "   rating  timestamp  \n",
+       "0       4  887736532  \n",
+       "1       5  875334088  \n",
+       "2       4  877019411  \n",
+       "3       4  891700426  \n",
+       "4       3  883601324  "
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "all_ratings.head()"
    ]
@@ -164,9 +601,151 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 8,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>item_id</th>\n",
+       "      <th>release_date</th>\n",
+       "      <th>video_release_date</th>\n",
+       "      <th>release_year</th>\n",
+       "      <th>user_id</th>\n",
+       "      <th>rating</th>\n",
+       "      <th>timestamp</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>100000.000000</td>\n",
+       "      <td>99991</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>99991.000000</td>\n",
+       "      <td>100000.00000</td>\n",
+       "      <td>100000.000000</td>\n",
+       "      <td>1.000000e+05</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>425.530130</td>\n",
+       "      <td>1988-02-09 00:43:11.369223</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1987.956216</td>\n",
+       "      <td>462.48475</td>\n",
+       "      <td>3.529860</td>\n",
+       "      <td>8.835289e+08</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1922-01-01 00:00:00</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1922.000000</td>\n",
+       "      <td>1.00000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>8.747247e+08</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>175.000000</td>\n",
+       "      <td>1986-01-01 00:00:00</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1986.000000</td>\n",
+       "      <td>254.00000</td>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>8.794487e+08</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>322.000000</td>\n",
+       "      <td>1994-01-01 00:00:00</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1994.000000</td>\n",
+       "      <td>447.00000</td>\n",
+       "      <td>4.000000</td>\n",
+       "      <td>8.828269e+08</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>631.000000</td>\n",
+       "      <td>1996-09-28 00:00:00</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1996.000000</td>\n",
+       "      <td>682.00000</td>\n",
+       "      <td>4.000000</td>\n",
+       "      <td>8.882600e+08</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>1682.000000</td>\n",
+       "      <td>1998-10-23 00:00:00</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1998.000000</td>\n",
+       "      <td>943.00000</td>\n",
+       "      <td>5.000000</td>\n",
+       "      <td>8.932866e+08</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>330.798356</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>14.155523</td>\n",
+       "      <td>266.61442</td>\n",
+       "      <td>1.125674</td>\n",
+       "      <td>5.343856e+06</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "             item_id                release_date  video_release_date  \\\n",
+       "count  100000.000000                       99991                 0.0   \n",
+       "mean      425.530130  1988-02-09 00:43:11.369223                 NaN   \n",
+       "min         1.000000         1922-01-01 00:00:00                 NaN   \n",
+       "25%       175.000000         1986-01-01 00:00:00                 NaN   \n",
+       "50%       322.000000         1994-01-01 00:00:00                 NaN   \n",
+       "75%       631.000000         1996-09-28 00:00:00                 NaN   \n",
+       "max      1682.000000         1998-10-23 00:00:00                 NaN   \n",
+       "std       330.798356                         NaN                 NaN   \n",
+       "\n",
+       "       release_year       user_id         rating     timestamp  \n",
+       "count  99991.000000  100000.00000  100000.000000  1.000000e+05  \n",
+       "mean    1987.956216     462.48475       3.529860  8.835289e+08  \n",
+       "min     1922.000000       1.00000       1.000000  8.747247e+08  \n",
+       "25%     1986.000000     254.00000       3.000000  8.794487e+08  \n",
+       "50%     1994.000000     447.00000       4.000000  8.828269e+08  \n",
+       "75%     1996.000000     682.00000       4.000000  8.882600e+08  \n",
+       "max     1998.000000     943.00000       5.000000  8.932866e+08  \n",
+       "std       14.155523     266.61442       1.125674  5.343856e+06  "
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "all_ratings.describe()"
    ]
@@ -180,7 +759,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -190,36 +769,240 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 10,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "items['popularity'].plot.hist(bins=30);"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 69,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "np.int64(141)"
+      ]
+     },
+     "execution_count": 69,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "(items['popularity'] == 1).sum() # Number of movies with only one rating"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 70,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "49                  Star Wars (1977)\n",
+       "257                   Contact (1997)\n",
+       "99                      Fargo (1996)\n",
+       "180        Return of the Jedi (1983)\n",
+       "293                 Liar Liar (1997)\n",
+       "285      English Patient, The (1996)\n",
+       "287                    Scream (1996)\n",
+       "0                   Toy Story (1995)\n",
+       "299             Air Force One (1997)\n",
+       "120    Independence Day (ID4) (1996)\n",
+       "Name: title, dtype: str"
+      ]
+     },
+     "execution_count": 70,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "items.nlargest(10, 'popularity')['title'] # Get the 10 most popular movies"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 71,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>item_id</th>\n",
+       "      <th>popularity</th>\n",
+       "      <th>release_date</th>\n",
+       "      <th>video_release_date</th>\n",
+       "      <th>release_year</th>\n",
+       "      <th>user_id</th>\n",
+       "      <th>rating</th>\n",
+       "      <th>timestamp</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>100000.000000</td>\n",
+       "      <td>100000.000000</td>\n",
+       "      <td>99991</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>99991.000000</td>\n",
+       "      <td>100000.00000</td>\n",
+       "      <td>100000.000000</td>\n",
+       "      <td>1.000000e+05</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>425.530130</td>\n",
+       "      <td>168.071900</td>\n",
+       "      <td>1988-02-09 00:43:11.369223</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1987.956216</td>\n",
+       "      <td>462.48475</td>\n",
+       "      <td>3.529860</td>\n",
+       "      <td>8.835289e+08</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1922-01-01 00:00:00</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1922.000000</td>\n",
+       "      <td>1.00000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>8.747247e+08</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>175.000000</td>\n",
+       "      <td>71.000000</td>\n",
+       "      <td>1986-01-01 00:00:00</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1986.000000</td>\n",
+       "      <td>254.00000</td>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>8.794487e+08</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>322.000000</td>\n",
+       "      <td>145.000000</td>\n",
+       "      <td>1994-01-01 00:00:00</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1994.000000</td>\n",
+       "      <td>447.00000</td>\n",
+       "      <td>4.000000</td>\n",
+       "      <td>8.828269e+08</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>631.000000</td>\n",
+       "      <td>239.000000</td>\n",
+       "      <td>1996-09-28 00:00:00</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1996.000000</td>\n",
+       "      <td>682.00000</td>\n",
+       "      <td>4.000000</td>\n",
+       "      <td>8.882600e+08</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>1682.000000</td>\n",
+       "      <td>583.000000</td>\n",
+       "      <td>1998-10-23 00:00:00</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1998.000000</td>\n",
+       "      <td>943.00000</td>\n",
+       "      <td>5.000000</td>\n",
+       "      <td>8.932866e+08</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>330.798356</td>\n",
+       "      <td>121.784558</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>14.155523</td>\n",
+       "      <td>266.61442</td>\n",
+       "      <td>1.125674</td>\n",
+       "      <td>5.343856e+06</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "             item_id     popularity                release_date  \\\n",
+       "count  100000.000000  100000.000000                       99991   \n",
+       "mean      425.530130     168.071900  1988-02-09 00:43:11.369223   \n",
+       "min         1.000000       1.000000         1922-01-01 00:00:00   \n",
+       "25%       175.000000      71.000000         1986-01-01 00:00:00   \n",
+       "50%       322.000000     145.000000         1994-01-01 00:00:00   \n",
+       "75%       631.000000     239.000000         1996-09-28 00:00:00   \n",
+       "max      1682.000000     583.000000         1998-10-23 00:00:00   \n",
+       "std       330.798356     121.784558                         NaN   \n",
+       "\n",
+       "       video_release_date  release_year       user_id         rating  \\\n",
+       "count                 0.0  99991.000000  100000.00000  100000.000000   \n",
+       "mean                  NaN   1987.956216     462.48475       3.529860   \n",
+       "min                   NaN   1922.000000       1.00000       1.000000   \n",
+       "25%                   NaN   1986.000000     254.00000       3.000000   \n",
+       "50%                   NaN   1994.000000     447.00000       4.000000   \n",
+       "75%                   NaN   1996.000000     682.00000       4.000000   \n",
+       "max                   NaN   1998.000000     943.00000       5.000000   \n",
+       "std                   NaN     14.155523     266.61442       1.125674   \n",
+       "\n",
+       "          timestamp  \n",
+       "count  1.000000e+05  \n",
+       "mean   8.835289e+08  \n",
+       "min    8.747247e+08  \n",
+       "25%    8.794487e+08  \n",
+       "50%    8.828269e+08  \n",
+       "75%    8.882600e+08  \n",
+       "max    8.932866e+08  \n",
+       "std    5.343856e+06  "
+      ]
+     },
+     "execution_count": 71,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "all_ratings = pd.merge(popularity, all_ratings)\n",
     "all_ratings.describe()"
@@ -227,7 +1010,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 22,
    "metadata": {
     "collapsed": false
    },
@@ -238,9 +1021,134 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 23,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>item_id</th>\n",
+       "      <th>title</th>\n",
+       "      <th>release_date</th>\n",
+       "      <th>video_release_date</th>\n",
+       "      <th>imdb_url</th>\n",
+       "      <th>release_year</th>\n",
+       "      <th>user_id</th>\n",
+       "      <th>rating</th>\n",
+       "      <th>timestamp</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>Toy Story (1995)</td>\n",
+       "      <td>1995-01-01</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>http://us.imdb.com/M/title-exact?Toy%20Story%2...</td>\n",
+       "      <td>1995.0</td>\n",
+       "      <td>308</td>\n",
+       "      <td>4</td>\n",
+       "      <td>887736532</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1</td>\n",
+       "      <td>Toy Story (1995)</td>\n",
+       "      <td>1995-01-01</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>http://us.imdb.com/M/title-exact?Toy%20Story%2...</td>\n",
+       "      <td>1995.0</td>\n",
+       "      <td>287</td>\n",
+       "      <td>5</td>\n",
+       "      <td>875334088</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>1</td>\n",
+       "      <td>Toy Story (1995)</td>\n",
+       "      <td>1995-01-01</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>http://us.imdb.com/M/title-exact?Toy%20Story%2...</td>\n",
+       "      <td>1995.0</td>\n",
+       "      <td>148</td>\n",
+       "      <td>4</td>\n",
+       "      <td>877019411</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>1</td>\n",
+       "      <td>Toy Story (1995)</td>\n",
+       "      <td>1995-01-01</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>http://us.imdb.com/M/title-exact?Toy%20Story%2...</td>\n",
+       "      <td>1995.0</td>\n",
+       "      <td>280</td>\n",
+       "      <td>4</td>\n",
+       "      <td>891700426</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>1</td>\n",
+       "      <td>Toy Story (1995)</td>\n",
+       "      <td>1995-01-01</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>http://us.imdb.com/M/title-exact?Toy%20Story%2...</td>\n",
+       "      <td>1995.0</td>\n",
+       "      <td>66</td>\n",
+       "      <td>3</td>\n",
+       "      <td>883601324</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   item_id             title release_date  video_release_date  \\\n",
+       "0        1  Toy Story (1995)   1995-01-01                 NaN   \n",
+       "1        1  Toy Story (1995)   1995-01-01                 NaN   \n",
+       "2        1  Toy Story (1995)   1995-01-01                 NaN   \n",
+       "3        1  Toy Story (1995)   1995-01-01                 NaN   \n",
+       "4        1  Toy Story (1995)   1995-01-01                 NaN   \n",
+       "\n",
+       "                                            imdb_url  release_year  user_id  \\\n",
+       "0  http://us.imdb.com/M/title-exact?Toy%20Story%2...        1995.0      308   \n",
+       "1  http://us.imdb.com/M/title-exact?Toy%20Story%2...        1995.0      287   \n",
+       "2  http://us.imdb.com/M/title-exact?Toy%20Story%2...        1995.0      148   \n",
+       "3  http://us.imdb.com/M/title-exact?Toy%20Story%2...        1995.0      280   \n",
+       "4  http://us.imdb.com/M/title-exact?Toy%20Story%2...        1995.0       66   \n",
+       "\n",
+       "   rating  timestamp  \n",
+       "0       4  887736532  \n",
+       "1       5  875334088  \n",
+       "2       4  877019411  \n",
+       "3       4  891700426  \n",
+       "4       3  883601324  "
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "all_ratings.head()"
    ]
@@ -260,13 +1168,28 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 11,
    "metadata": {
     "collapsed": false
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "   item_id    rating\n",
+      "0        1  3.878319\n",
+      "1        2  3.206107\n",
+      "2        3  3.033333\n",
+      "3        4  3.550239\n",
+      "4        5  3.302326\n"
+     ]
+    }
+   ],
    "source": [
-    "raise NotImplementedError(\"Please calculate the average rating for each movie\")"
+    "# average rating for each movie\n",
+    "avg_ratings = all_ratings.groupby('item_id')['rating'].mean().reset_index()\n",
+    "print(avg_ratings.head())"
    ]
   },
   {
@@ -278,7 +1201,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -318,7 +1241,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -329,7 +1252,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -376,9 +1299,38 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 15,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 1/10\n",
+      "\u001b[1m1125/1125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - loss: 2.6405 - val_loss: 1.0455\n",
+      "Epoch 2/10\n",
+      "\u001b[1m1125/1125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 2ms/step - loss: 0.8504 - val_loss: 0.7936\n",
+      "Epoch 3/10\n",
+      "\u001b[1m1125/1125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 2ms/step - loss: 0.7574 - val_loss: 0.7676\n",
+      "Epoch 4/10\n",
+      "\u001b[1m1125/1125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 2ms/step - loss: 0.7338 - val_loss: 0.7594\n",
+      "Epoch 5/10\n",
+      "\u001b[1m1125/1125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 2ms/step - loss: 0.7149 - val_loss: 0.7495\n",
+      "Epoch 6/10\n",
+      "\u001b[1m1125/1125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 2ms/step - loss: 0.6953 - val_loss: 0.7454\n",
+      "Epoch 7/10\n",
+      "\u001b[1m1125/1125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 2ms/step - loss: 0.6738 - val_loss: 0.7394\n",
+      "Epoch 8/10\n",
+      "\u001b[1m1125/1125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 2ms/step - loss: 0.6522 - val_loss: 0.7375\n",
+      "Epoch 9/10\n",
+      "\u001b[1m1125/1125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 2ms/step - loss: 0.6288 - val_loss: 0.7358\n",
+      "Epoch 10/10\n",
+      "\u001b[1m1125/1125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 2ms/step - loss: 0.6038 - val_loss: 0.7361\n",
+      "CPU times: user 40.3 s, sys: 4.43 s, total: 44.7 s\n",
+      "Wall time: 27.8 s\n"
+     ]
+    }
+   ],
    "source": [
     "%%time\n",
     "\n",
@@ -390,9 +1342,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 16,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "plt.plot(history.history['loss'], label='train')\n",
     "plt.plot(history.history['val_loss'], label='validation')\n",
@@ -417,7 +1380,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -432,9 +1395,29 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 18,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[1m625/625\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step\n",
+      "Final test MSE: 0.902\n",
+      "Final test MAE: 0.731\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYAAAAF4CAYAAABdBi7LAAAAOnRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjEwLjgsIGh0dHBzOi8vbWF0cGxvdGxpYi5vcmcvwVt1zgAAAAlwSFlzAAAPYQAAD2EBqD+naQAAfNRJREFUeJztvXmYbFV5qP+uYe9dVT2dw2FWBvkpEqKgETBKEsUhij6YmDxRCUZAH280OCJRiVE0Jh6913jNYET0RswkejUkueaiUYOiQSNinJLrgFEhCiJwTk9Vtfdew++Ptau6z8iptptV1b3e52l2195fn/Odpmp9a32j8N57EolEIrHlkLEVSCQSiUQckgFIJBKJLUoyAIlEIrFFSQYgkUgktijJACQSicQWJRmARCKR2KIkA5BIJBJblGQAEolEYouSDEAikUhsUZIBSCQSiS3KRBmAH/zgBzz72c9mx44dtNttHvrQh/LFL34xtlqJRCIxkejYChwqu3bt4uyzz+acc87huuuu44gjjuDb3/4227dvj61aIpFITCRiUprBvfrVr+Zf/uVf+MxnPhNblUQikdgUTIwBOPXUU3nSk57Ef/3Xf/HpT3+a+93vfvzWb/0Wz3/+8w/4M2VZUpbl8LVzjnvuuYcdO3YghLgv1E4kEokNxXvP4uIixx57LFKO6NX3E0JRFL4oCn/55Zf7L33pS/5d73qXb7Va/uqrrz7gz1xxxRUeSF/pK32lr03/ddttt428rk7MCSDPc8444wxuvPHG4b2XvOQl3HTTTXzuc5/b78/sfQKYn5/n+OOP57bbbmN2dnbDdU4ktiLGOpwHKdjjpO29H97XajzyT5zzGOfxzmOcwzlwhOwYKUFLiZACLQVSjqfXYGFhgeOOO47du3czNzc30s9OTBD4mGOO4dRTT93j3k/91E/x4Q9/+IA/UxQFRVHsc392djYZgERiAxgsqOogi6V1fqwW1KVeRWWhEA6lFM6Fxd9ai/GSXMF0O4+t5r2yFrf2xBiAs88+m29+85t73PvWt77FCSecEEmjRCKxmrDD9wzW9YFzwXsYrE1CCKQA5z3Cr23RWk+McSAkufSUxtM3BmNBK1ACCi1ACIxxaD0ep5b1ZGIMwMtf/nIe/ehH86Y3vYlnPOMZfOELX+Cqq67iqquuiq1aYkI50AKVWDveg5QC54Ix8D44qAXhdyzFyvPYOOcwzmGMwTiBILh8pAwuIO8dfePQ0mOkRjpGD7KOORPzrznzzDO59tpref/7389DHvIQ3vjGN/L2t7+dCy64ILZqiQnEOY91HmODy8LY8HocFqZJZbDYG+uCX917hFhZ/L0f/K5diFqOwa+6qh2VDUZJSomUAtW4pwavKxvkNiMTEwReDxYWFpibm2N+fj7FANaZSdpNT1KQcpLw3lPVDkdY9Pd/AhB4ws4zz2TU90hVGXb3aqQQKCWxjWEavjdgeN95z7Z2Rp6Pn9PkJ1nXxu9fk5g47u24P04EXdlvkFIIgRIMTwLjpvu4473H47E2+NX3/v8/OAHgHULJ5oQQ73cshMdZj9SCyjictSAE1oEKPiCsD7EAZz1CbL69cjIAiZ+I1bvp/X3Ypfdjs5veO0h5IMYpSLmacT9lCSHwDqwX5OrABrYyAuXi6+4cWDy2tnhCXGJwArAOBB7pHQYPIshvNpIBSKyZSdxND4KUB0OI8QhSrmYSTlnOebwALcP/9wO52LQEL4K82o+huK+QUiA8LPYNeabRUqBWBXmdc9TWU9WGmZYem9/zepIMQGJNTOJuerB4HpIse+60YzIppywhAA+ZVqEewFp8s5tWMjzXSiGlxBg3Br/bUEQrmneFMSbo5kGLYACQunk++NpcJAOQWDOTtpseZKQckizjsfhP0inLN0cT731jAILhMg70wIAJGX6vIn4WkPfhq5VrrHPML5dUDqoa8gxyCXPTklauh7KbjfjbhsREspbd9DgwSEc8GIP0xdiMesqKndAnpUAJQbc0dCuL8yCERCuJEBLnoVtZuqVBifiVwEIItFYs9Sp+NF9SViEjSCqBFIKyqvnRfMlSr0JrFf0EuxEkA5BYE5O4mw5VqIJ7O5AEd4sYiw+8PwTXmRBibAys956ytiFO0Szyg7x60aSAlrWNbqwg/G6r2rJcWhBQ15Z+WdPt1/TLmroO95dLS1XbsfkdryfJBZRYM4Pd9MEWqHHZTQ+QUiC9P2iQclwCq5MWs/A+uH20kuRaYp1rcugHNRdQaElFkMt93DoA7y29qsIYi1eSfmUxeCoLRoHG0Wk5vHX0qgrvMzbbkrm5/jWJ+4xBTxfjPAdL5AhZH+Oxmx6glRxm1QxS/wanlHFqUjZpp6yBAQ2L/8AltaeBtc6T6+AOin0KcM7Rry3zC12czNHek2caSegFZOqS3fMV0lVMtVUICm8ykgFIrJlJ2k3vzSAFEMY3rx4m65TlHNjG4MNAZzGsqh0ghMA4j3OgVCRlAWNhd7+m8oJWo1Nd1dQeMhvqALQU9L1gd7/G2Hi6bhTJACR+IiZlN70/BovqOCye+2PSTlkDY+W8INNyuGMetFeG0F9ncDqI/Xuv64reUoXyjq6pqas+yIzSQqEAV5Plisw7eksVdV0B7bhKrzPJACR+YiZlNz2JTNYpq8mp9w7nBm6eJj7hmvfFoBXEGOTVh26fNd5BBfSrGis9fQu1AuUMXlnAYYzD++QCSiT2y7jvpieZwSnLOjesCxgEVXXTsXIckDKkfHYrS6bFAQvXauPo5Cp6a2UrBJWz7FruUxRtjA0tHwZ2ylmPN4bdZY/t0y3sJnxzJwOQSCTWiUErBYt1HoRHSjk8FTrnsD4sokEu7oKqfBgDudjvUXqJL/tkCOpwSKEu+wgUVdljppOjNmEeaDIAicSYs9IKosmrb+6PWyuIEPiF6ZamNI5+v8Ihhj51iafVKujkujkp7pkldF9jjKWqa4SH2tbcNb8bn3XpW2gpEHXF4TtyhIeqrjGbMAqcDEAiMcasbgXhm2rf1XEWJcXYtIJYjbGOsrYYBP0aXAYaj84cxbiMVpQC42CxLKkqw1LXIYuKyjfN6kqHaHXJvWXOdbjXkuwJJBmARGJMWd0K4qDdQMem4V6oSL5roY9xAi0lbaXJdeipb61hsWfplYYjZtvEdgE556m85e6FZShy6gpabRAWhIS6gsV+H8qKIw+bHZueVutJMgCJxBjjfVjcnQ956UII8KtGLA6brI3D7tRTGkO/dhS5xrtwOrHhER6BUoJ+ZSmNoZXHjQPUdc3C4iIIUEKw2IPSOvoGWhp8Ba3tAitgYXGRuq6j6bpRJAOQSIwpoZXySm1FyKD0+5wABlW1SoqoWVjGGLq9mlwNxkNaHI7aQSZDDCDPJbmCbq9mKpfkeR5R34rFqk93GURVsrQIWQd6Dqih7oJeLPE1LE73MaaKputGkQxAIjG2+KEbyHPgtEqBb04AcYOq3kPpPLUFb0PKarBQ4JshMWXtEIAXPnpztdpBtzTML4AsYKGGog+lBaugrEEugSthdpthM86FTwYgkRhjnPM4QCuBc67ptT+orA3plMaOR7N6Yz39uimaQoZKcCRaNycYBgbAYX3Qu4iob1nVdOdh9xLkNewGWiUYoG+gD+glqErYMR/kNxvJACQSY8rqbqDGOIwL6aDD6l8RxisixmPmgsBRG4MgdAOtqhKwlB6KJu0zzwtqM5CLu6U2VY9dC7AItEuYB2qCAdBAF+iU0AN2LQT5zUYyAInEmCKa4G6/NlQWMulRSiGbILC1lmXjyRW0Mh29Ctt7j7Nh1GNZG+6+Z4Haw4KBWQ2ZgB07toMPbRVidwPtVjXzi2HnLwg7fgGUQNG87hOeb1sM8puNZAASiTHFezDO4RG0tKAyhtqY4Q5VSmhphW0qWr2Pm18vpSLPNbf/aJG7a0O9PE+tC2oLywoyU3JXDTsyzTFHzSBlxFaghFNVl5VFHsJuf/V1d/O828hvNpIBSCTGFo93PrRQQDQZQQLvwMmQEVTZ0FjNC0ns5mqD+c/fu+seekIi+11a05rKggLml7s4q1j0jqOOmI7eLLDb7+2z8O/NakPQ7ScXUCKxaRi4IMa5g+lghKJDonAopfCN/99aR+0EEkc2Bq0gjKm5Y2GBvjdkqkUPQb9X0nNQSpAI2lLSNxV3LCzwgB0tIF4aqLWG5UOUXW7kNxvJACTWhUlYTFcz6K7pVwVVhQhZNePSUsE5qKwDIdECun2L8ZbKQK5BC+i0FM5LKuuiD1gpyz5371qi2+szNd0iVxqd5eQecgEGh/XQ7QW58thZpqenoulbW8uhdvexjfxmIxmAxE/MJCymqzHWhdTJJm/eE/TGh3+HVmJMmqs5vPPUdU3XgPIOqTSZDr9jYw27ujUdDZnMAAfEswC9ynLnch+VFaEttLVUoqTvoCUBa+koicoK7lzu06viLqi9frmh8pNAMgCJn4jJWUwDznkq45ppW2KvAStB98q4YefNmHgPpbF0K0emdaOvZNBAQQqN9IJuZVDKNkPL49G1FUt1xbasxXyvy127d+FURkVw9Ehbc7jKmNaK3XWfro1bWVuO6NIfVX4SSAYgsWYmaTENOnmMDZkcSgpsc6S3dsV1opTC2CCXCRnVjeWcD/5/D61cNfp67KAQTAhypShrE+QiNytT1mFrw4968yx4MCXINrhmwpYr4falRWYFaK1QNm5WTW/Ev35U+UkgGYDEmth7Md0bIQRKMDaLKQSdrQs9c6qmIZl1YFwoqFISCg1ZFubW6nsZxr7xhN9vJ5d0S4M1NQhJbSFTgHcondHJJc77oXwshFD0ypIf/qjmsKOmUBloqREyLDQmqxFO8v0fLXPsURlCxE0DdSN6dEaVnwSSAUisidWL6eB1uO4ZBB70q4+/mK6cSnr9mn7t0dIjpSKTwaVinWWhb2hZQZHp6JW1zgk8Ipykaku3V+KQlCYYKomj05aoXAY5F/f3m0nQwlLXIT5RlaDpU/tQBGZKyLWhroNcFtkzWI3ogRpVfhJIBiCxJgaLqWpyvw8WBB70sR8Hqjr41ItMNe2VGbZXllKSSUG3sggsrcgrlFISpQR3zZdYD844UHL4e3bWsVQaehUcPlegIsdaamNQUlNkFQtLJUt3QGvOUznIJfTnYfrokiIDJTW1iZtW6Ue0l6PKTwLjE527F17/+tcjGj/z4OuUU06JrdaWx1hHZRzGutD7vflafX9ccM5SGYMSfthe2djQUdPYQc99UMJTGYNzsdP+HN5auss9+mWYsxtca+H9b52nX4bn3lpiu4CczrBKk3dAGFgqYf4e2L0rXJfKcD/vgFUapyMHrQ+1CGCN8pPARJ0Afvqnf5pPfOITw9daT5T6m4qwc/aUxhEmQYUtvmPQlMwjRDgZ5EpG71MzYNBMzTh/wPbK4zT4qW8MzkvahWJxfhlb1nQddCQoPDNzLXrLkn7k3TSArQ2mNHQXoRYhd77IQDdBa2ugX4EtwUwbbB1X53LEBX1U+UlgolZQrTVHH310bDUSDd6HLBWtFb7ZPrum9zveI6THGEs2BhlAEHzqzot7bZgw8LvH9qkb40JvfWe5c/cyda+H1Tm1AyNBmYqeV0wrQW2DfBGxv7Lyhm6/otuDzhxkEGYBNAeTDMgy6M5Dt1+hfFwD0N1g+UlgogzAt7/9bY499lharRaPetSj2LlzJ8cff/wB5cuypCxXQvcLCwv3hZpbBk8Y8uFqhxTBPeFpjEDTGdJ7H7lDzQpCeKSETEmsD900EQLrw8xavEcqRSuT1NYhRFzNnfP0asdSVbFkPcIaMp0jfPDdVtZQVhUoQaduRU8DrZyj14WlZcjaYcHMytBMrUVotVxX4XmvG+RjMuriN1GL5SEyMTGARz7ykVx99dV89KMf5Z3vfCff/e53+fmf/3kWFxcP+DM7d+5kbm5u+HXcccfdhxpvbpwL7pL9pYCuRkkR3Cpj4FcRItTJBtcP1MbSrw3LfUO/NtTGImV4riC628o4yz0Ly1TWM53nCKXomZquremZGqEU03lOZT33LCxjIscsBk3qloEf3AV3EYxAr7ne1dxfZtDMLqKyQDbi3z+q/CQwMUbt3HPPHX5/2mmn8chHPpITTjiBD37wgzzvec/b789cfvnlXHrppcPXCwsLyQisE957auPItEJKgbVuuPuXEgQSpSTOBbnYvd9h0K7YsWupQkrZpKxKtBrM3HV0+xbnHNun8+jtiq2pKPt9uv0KJ4K+edMMLhchC6hfG/r9Lkrk2Mgza5fLiqXFlQ6aS4SF3xF2mo5wEgBYWgzyMVke8a8fVX4SmBgDsDfbtm3j5JNP5pZbbjmgTFEUFDGdopuYwazarBlaIpoURK9WptIKIUB4au/HxAAIMiHpVwYvFG3tybRENWmgdW3plhbhLZloRa9e9l7Q9Y6udWAM3jrAYQZxFusQwtC1DuUdPnKeYl312b07DFQZ5Pe4/VxrYPfuIB+VUd+S8d/C687EuID2Zmlpie985zscc8wxsVXZkgzaPww8O4PUXLkqTRcGdQEiehEYgHOO2lpm2hkzLYV1nuWyZr5bs1zWWOeZaSlm2hm1DSeBmFTe46zHWku3rlnoLrNU9tjd77FU9ljoLtOta6y1OOupIhvZsuyzmzBi8Z4DyNzTPN/dyMfkv0Yc8DWq/CQwMSeAyy67jPPOO48TTjiBH/7wh1xxxRUopTj//PNjq7YlEUKQaTnM+w/FX3umVDofrpmO3wZioJNxHq0k3gusBOXB6+BSEQK0lAgR5GKfWrQPc4C1kiybiuVuF1dbSgeFBFmXTGUFU0oGuchO9dL44YCVe2N3Ix+TUT06m9ADNDkG4L/+6784//zzufvuuzniiCP4uZ/7OT7/+c9zxBFHxFZtSyKlIFeSqjnc+6baN/QEhZBMGVpB5Go82kK7pu+P81DbMGAdIRA+VHl6HyZsZUoEuchBP+M9OMs9C4tQdOj1S7QXlE3vorIs0W1LWXY5+rDZIB+RpeXlA07W2pteIx+TH2+w/CQwMQbgmmuuia1CYhXDE4APS/7AXTJoBQEeKYOHcbxOAC4Epq3HWo8XHmOb2IX3OOEQhO6lsU8AhcowHnbVhrasKW0Ytl47WJaAhayu6dWGw32Qj0ldj+YjGVV+vRk1xB83JWBjmBgDkBgvhAh9/rVrFtIm399aECpMBhCEfjZajYcBEKLxp5cWpTTe+zDBIMSqg9vKwXJd0ylU9DqAsq7AObq7S5Y7NfU8TG2z4MPpZHke+nIB0XVwuAvyEblnxDKbUeXXm90bLD8JJAOQWDNSCqSAqokEh53zigvIOk+mx2MWAIQWFcY5epWlVUhq43FNVk0tQeLJhKNfWfJMRG9gJ5Vgod+nmNUoJ7ltuWKp7+l7aAnwFo7bobGzjoV+H6kiD1nfvbHy6006ASQDkPgJcE3fnEzLZrEUqKELKEyvCi0V/NgYARCUlaFvQGPQWYZ3IQZQ1jXLlUc4A1Nx3SkAi90+vapi/k6DyaG/DJ1toJu01d4C3LlQoSvY3qpY7PaJmRP3490bK7/epCBwMgCJNbJ6IEymJcY4wOMsyOZdpbVsRkaOx0CYwQQzrRXGefqlwVchBqAVIf+/UGitqIyLXr2cCcFir0fPgrYgsqaZXVNxK7JwCuhZWOz1yCL/fheWNlZ+vRk1CTVy1cKGMLF1AIm47D1dq187lktH14Rrv3ZUlR0OhIkdUIVQB1CZkN8/rJmSTZ/35pPgxZ5yMdnV67J7HhbuAetDIzVdQFY01yzcX7gHds8H+ZgcKPd/veQT6086ASTWxOrpWpUFhUUpBWFmCdZalizk1o7FdC0IbhNjHf0y7PSVFAgp8CpMs/JegBf0S4vWInovoNw5en0gA52Dy0OarRfhqvJwnwx6/SAfk1F/XePiFNzKJAOQWDNVbcPiL6AyYGs77KypJOQaKstYTNeC0Lm0tg58aFNd9mscltJCocKIxUJo8EHORnYB7eqX1DVkHuoyDCTJHBgPVkDdA5E3z+sgH5NRoybxoyyJZAASa8I5i7GWsqxxQpNJT6YVmma+rrUslRbpDarIcE5GH1k4GABT1YZ+3yJcDSrHe6gdYGv6VtJSnvaUJnbcul3k6AzcFBgL8zVkdQhG5oSeOoWFbAq0DvIxOXBf3vWRT6w/yQAk1oQQgso6KieZaknq2mCcwXjQIhiBIlMs9yXauugBYGgqgWtHt6pRKgcEWgRltQCDQADdqmaqzqNXAnsfFvbdd4PJQh56GzAEI9ADWguga+gcRXQ3WzoBTB7JACTWRF07auuROBb7NbY2eCkxJixawjmU9Wg8tZXUtSP6BE/hKZ1BSkmr0JT9itqZ0FuHcEIoCk2vbyidCdVhEZnOcrQKffSn6tBlMydcRXOt62AYjlZBPiaj1nWl8Uzxif2RTEwoqqmKqW2YWqVwSEIVmHdgraO0lnYmUGpFPibOWjAehadb1lhjEUoGfQFjLbas0XgwPshHJMsVhYB5QpJSn/CB7RGCwP3m+3mgEEE+JqMemDbhfJWJIxmAxJowxtOvDWXtaBcZpvZ4QisFqUBKSZ4pyrIG4TEmJ4+7QcUjcUIEf78C6w3CC0oP0oP3BkmIBxRC4CNnSffKmm5TfTTYLS/tdR3c71ZBPiYpDXTySAYgsSaUApqmarmjafwmELpJqRce78IJoXB+LE4ASPA29P4x3rLU62OkobTQV6CdYSZro0UjFzlxqTY1/f5K0Hd/WMLzfj/IxyRV1k4eyQAk1oRzHqEEEke3sihfo7WmJgT3jDFYkSFxCKWiV9UCTac6R6/fZcEK+v0+Km/Tt4CHbtWnkl1mlWeqPRPkI3LX8jJ3LIb4xIHs5yDr6o7FIB+TUf/2uNomIBmAxJqReCsoigLrLbt3lZS+pKygyINPett2jRJF2E3H3k4TZgKXxvKj5T550UZKjRcS1VQDS6mxHn603GdmphN9JnC9vMxdBD//gT6ofUJW0F2NfCIxCskAJNaEEJ48l/x41zLzPU/mDEWrhQJ0Brbf5467+8y1BUdsn4reWhmgtpZuVdOt+ui8RaY0eZZTu1AJXDmL8Z5u1adb1dSRTwD92gx9/eYAMoP7S418IjEKyQAk1oQQgsoYun1HJ89w1uOdGGYBqSwnU5puv6YyBiFasVXGmZpuVbG9PYXWKgxXqfv0LLQVCGcoijbb21N0qwoX26duR8uTGVU+kUgGILEmnHOUtcEaS7vTwrgKhMMSWkGAR2eKfq9PWZvojdUAesZS1pbpIqcU0CtLTGXoObAStLe0OjNMFznduqZn4p4AtB/t7x9VPpFIBiCxJrx3WAOtlma5W2KqCic0xkEpQXqDtpJWS2NNkI+NVgq0Yr5fYbygX1WorBVmGTvo1xVLvZK+8GQtHeQjstAfzW02qnwikQxAYk3UFioHxjhKE5rUCx0KlBChEMwai0JQuSAfHe+RznL34gJO5/Tqmkxl9Jv++nVdY/o9pKk4Np+L3lvBj+iBGlU+kUgGILEmpIDKGMra084yLA4hJOiQs+51htIZZV0jjYneWA0gUwLjDfcsLzM9l1FVDiP61M1QeFc5sinP0vIyRx42RRZ5xOLd8xsrn0gkA5BYE9bUmKoKQ9VVhqs9Hoe1YBQIH4q/RG0wlcVGDqhC2NAL4zEVLHV79LqgvKFykEuwPfBFD1MFudjN1XojTswaVT6RSAYgsSaUUggpqayF2lJZi8NTN4NiJK65D1khw7CYyJTG0rce66DsllRdKLKmS6iEsguuKNEO+tZTRg4C90acQTiqfCKRDEBiTTgh0XlGb77LsnEUwpBnbUQzEKaqK5a7faSvmcvncCJ+IZh1lt1lD12AlopebSmXCN1AJdgaWrmCRs66uAZgsbex8olEMgCJNSG9xxtHK8uxQlL1e6E9sYVMgTA1RatAeYE3DhnbnwIsd3ssLC5jS3CZpXIgmmD2IBOoNhZZw8LiMsvduCuqHbGua1T5RCL+tiwxkThnsc6QS0GuJcY6enXNQlnTq2uMdeRakkuBdQYXeTcNkAlBVcFdu6DbB2NCMFsRrsaE+3ftgqoK8jFJM3YTG006ASTWhFQKKRW7yy5CFkghmFI5SkJLgPUlS32DdyVHd2aQYxAD6FlHv4KFBfASagPSNuMgCa97S7C4AHNzQT4mo8Z0Uww4MSrJACTWhPciLKK1B22gGariHVQqXLEGjMfLIB8bhcVVoX9ObaHngCrM23UqvFY2PHdVkI9JmrGb2GiSAUisCWtrym5Nrjw1Auss0lpqH3bVzoUisEx5ym6NtfHTQPtlTbcMrhLvw1B11WsWfMJrH8YF0C2DfEzSCSCx0SQDkFgTAug7g1Q5M0XOou1Te4uxYVhMphUz7ZyqDHLx9//gpMA1C7wQYcGHZhwk4XUzIx7ng3xMRs3qTFmgiVFJBiCxJirj8VbQK3shi8Z5WlmOkGEWgKlLlnoVtu6jVZvKxM8CEs6jZVjse73w5p/OwHjQAhbrcN8AWgb5mKQBK4mNJhmAxJrIc4nQAqE0lXNYY+gjMQ5qCcIYlHJopRFakOdjkHAmBcaFrJ+ZKejOQ78OO+cWoY/RzBTMz4NxQT4moyahpjKAxKgkA5BYE2Vl8LXBmwovc7r9PihH6cMJAFsxnbXxrsLXkrKKn6SeIciaNb2qw4JpCLNpBzGAqvELZSLIx6TcYPlEYgy2ZWvjzW9+M0IIXvayl8VWZUvSzjU+Eyxbj/GOytaUtmKprihtRWVrjHcsW4/PBO08/l5D5Zr2dHCV7OqGoOkg0dMRXu/qhuft6SCfSGxmJvIdftNNN/Gud72L0047LbYqW5bSOEQFu5fmKVUBVUXeLvBNcLWqKpYWFylsydHTHUoTfx6AlFloVUF44w+CpgMXUJ+VD4TwQT6R2MxM3AlgaWmJCy64gHe/+91s3749tjpbFi0Ey95QC4nSEoSk8g7rHJV30NyvhWTZG3TkqloAW/cxJuz0BwFTs9d1mfDcmCCfSGxmJs4AXHLJJTz1qU/lCU94wr3KlmXJwsLCHl+J9aFXlfS6PagtLaEptGZKKYpMhavWtISG2tLr9uhV8T3U3nu6S2GxH5R47e9qgO5SkE8kNjMT5QK65ppr+NKXvsRNN910SPI7d+7kDW94wwZrtTVx1rFoaqY6bVpFxmIpqa3FNllAUkg6RYZ0bRZNjRuDgeUL/Zpdy6Fidltzz+91tYTnu5aDfCKxmZmYE8Btt93GS1/6Uv76r/+aVqt1SD9z+eWXMz8/P/y67bbbNljLrYPBIxBhV11W9KsSay19Z8O1KumWFd4HOUP83bSteizTFLEdQKbfPF9u5BOJzczEnABuvvlm7rzzTn7mZ35meM9ayw033MCf/umfUpblPkNHiqKgKIr7WtUtgXACLwSlqehaR1mWICTGglVAWVIXJR1vaWcFwsWPAexa7rKbsNs/mAEA2N3IJxKbmYkxAI9//OP52te+tse9iy++mFNOOYVXvepVYzFxaiuRZxKN556lLsXUFNaCMA7rQHrwFrxz3LPc5YR2Tp7FP2xqoQ45V75s5BOJzczEGICZmRke8pCH7HFvamqKHTt27HM/cR8gQApPWRrILd6HwlnRfDkPZW0pS4MUfiya1Zem4lC9+nUjn0hsZibGACTGCyUkEgkCPI6qBCUragtegS2hNeWCoUCixmAkpHSjBaJHlU8kJo2JNgCf+tSnYquwZelVBuOhlQvqytBdgNx6uh46AqplyKYNrVxgfJCPjR/RCI0qn0hMGhNtABLxkAL6taHVmqIquyz1wFkoDXQ1yApmhaFVTNGvTey+aoFR40QprpTY5CQDkFgTZV3Tqytu/f4ytCHPYPqwMF4xk7B0N+yed+zuLfH/nTRFWcfPqbfVaJW9o8onEpNGOuMm1kSRKRZ6y/QNTHckWSfM1B18ZZ1wv29gobdMkcXfTdfVaLUIo8onEpNGMgCJNbFrYZHuIlQ9WFx2LC+HVsp1Ha7Ly+F+1YPuYpCPTX/EmO6o8onEpJFcQIk1kSsFMrRNthaqMmT/DFxAdQm5Dc+RjXxkyhELe0eVTyQmjXQCSKyJeypDrw9lPwR/jQ0VtoMvY5ugcB96/SAfm2rEtP5R5ROJSWPkE8DDH/5wxH5a+wohaLVaPPCBD+Siiy7inHPOWRcFE+PJrATvQGfhS6mw4DsXisCUCvddFuRmx2CroUbUYVT5RGLSGPkt/uQnP5n//M//ZGpqinPOOYdzzjmH6elpvvOd73DmmWdy++2384QnPIG///u/3wh9E2NCaQ3Ohvm5dQ1SQlFA0QpXKcP9+fnmJGDjnwDkiNudUeUTiUlj5Lf4XXfdxSte8Qpe+9rX7nH/93//9/n+97/PP/3TP3HFFVfwxje+kV/6pV9aN0UT48V0e5qpFkgFWRaaqBkDNsyCAcJ9qWCqFeRjM2oUIn7UIpHYWEY+AXzwgx/k/PPP3+f+s571LD74wQ8CcP755/PNb37zJ9cuMbYYZ8mmQAtY2A0LVZMBZMJ1oQr3tYBsKsjHZtTebqkXXGKzM7IBaLVa3Hjjjfvcv/HGG4d9+p1zh9yzPzGZaCnJgcN3wFQn3Ov1QsC312TPTHXC87yRj40fUYVR5ROTxagTnzfjhOiRXUAvfvGLecELXsDNN9/MmWeeCYQh7e95z3v4nd/5HQA+9rGP8bCHPWxdFU2MF1IrfAZLPZjeDrO7oDMNlYNchpGKrSlY2gXbjwjy0Rl1KmX8KZaJDeRY4Psjym82RjYAv/u7v8sDHvAA/vRP/5S//Mu/BODBD34w7373u/n1X/91AF7wghfwwhe+cH01TYwVEig8FDkoAQiobPhChddKhOeFH4984+URF/RR5ROTxag+is3o01hTnsMFF1zABRdccMDn7XZ7zQolJgTn8UojnGF5CboGWhWUDrDQN7C8BNqBVzrkhkZmaWFj5ROTxfYNlp8E1pzoVlUVd955J26vnunHH3/8T6xUYgLQGoWgV4FuhXkvUgIuXAWhIKxXgUKAjp9TOWoiavzE1cRG0tlg+Ulg5E/lt7/9bZ773OfuEwj23iOEwNr42R6JjccaQ7eqcQ4QKwO/BsVTovmPc9CtaqyJv5x2R2zuOap8YrIY9Uwa/wy7/oxsAC666CK01nzkIx/hmGOO2W9VcGLzY+uSXh9cDY6m/YMH75sr4b6rQ2aQreM71GdHfLePKp+YLFJdyBoMwJe//GVuvvlmTjnllI3QJzEhVBbqPjgJwgd3ia/A+hD8tYT7Tga5agwOhssjHkJGlU9MFqNuXTfjVnfk5IxTTz2Vu+66ayN0SUwQBkcloV/C0hJUrLh/lAyvl5bC80oG+dgce+TGyicmi1E9fJvRIziyAXjLW97CK1/5Sj71qU9x9913s7CwsMdXYotgLKbbdP+0YcHvWeg116q57wHTDfKxWRzRCzWqfGKymN1g+UlgZBfQE57wBAAe//jH73E/BYG3FtY5ehUsAdsV2Dos+gOviQVQsFTDVBXkY9MZcbszqnxispiaAUaYUzQ1s2GqRGNkA3D99ddvhB6JCSNTkpYIi363H4pmNSvB37K5XwEtEeRjszRif/9R5ROTxew2RjIAs9s2SJGIjGwAHvOYx2yEHokJo/Ier8OOvwd0CT1/qubabe4bwOsgH5veiC6dUeUTk0XqBXSIBuCrX/0qD3nIQ5BS8tWvfvWgsqeddtq6KJYYbyTB7aNZ2flXQN0896w8s/V4tILQI9qgUeUTk8WoDQs2Y4ODQzIAD3vYw7jjjjs48sgjedjDHoYQAr+fHV2KAWwd6rKmVwZfvyTsjjLCwj/4XhKe98ogH5vUCyixGlFsrPwkcEgG4Lvf/S5HHHHE8PtEQiiB9yE3OgfaQMGKMXDNfUEoDhMqfhZ1MaIKo8onJotsxP+/o8pPAodkAE444YTh99///vd59KMfjd6rt4sxhhtvvHEP2cTmpekAAYRdfkZY8CXhTWWa+zRy8XOAgotqI+UTk8WoIyrGYKTFujPyP+mcc87hnnvu2ef+/Px8GgS/hRAeVBYaZOWqyfdf9eWb+x2CnBgDf/rs1MbKJyaLfESXzqjyk8DIBmCQ7783d999N1NT6ROzVVBKUujg+tEq7JYrQvrn4HutGteQDvKx2bVrY+UTk0UxYlrPqPKTwCGngf7Kr/wKEAK9F110EUWxYg6ttXz1q1/l0Y9+9PprmBhL8jynMwM/XgZZhTJ5TVj4Jc2Q+CqcBjozQT42o55CxuHUktg4ilYbSe+Q3JOykd9sHLIBmJubA8IJYGZmZo+hL3me87M/+7M8//nPX38NE2NJoTNaGSzTLPasBIBl83qBEAdoZUE+NksjNncbVT4xWcx2OhxOjzsPQfbwRn6zccgG4L3vfS8AJ554Ipdddlly92xxlBRY1+zwWckAorkWzdcCYF2Qj01q/pVYTTvPmIZDMgDTjfxmY+RK4CuuuGIj9EhMGHVVU5Vhkc9lGP0IK0MzdHO/cFCVQT42yQAkVtPKc2YUK+lqB2FGBfnNxpoicx/60Id4xjOewc/+7M/yMz/zM3t8bRTvfOc7Oe2005idnWV2dpZHPepRXHfddRv29yUOTmUt1ocg72AgjGdlFOSgJ1CbMCOgGoMCwVFTUcchdTWxcXgk4hAnvYtWkN9sjPwv+uM//mMuvvhijjrqKP7t3/6Ns846ix07dvCf//mfnHvuuRuhIwD3v//9efOb38zNN9/MF7/4RR73uMfxS7/0S/z7v//7hv2diQPTs5a+aaYkrZoABisGAR+e902Qj830BssnJovK1dhe+P5ADsphrUsvyG82RjYAf/Znf8ZVV13Fn/zJn5DnOa985Sv5+Mc/zkte8hLm5+c3QkcAzjvvPJ7ylKfwoAc9iJNPPpk/+IM/YHp6ms9//vMb9ncmDoI1VGVwkwgal8+qL93c7xNcQNj4EdVRD/Cb78CfWE1VVux24f/z0c29NmFRHKS4HE14vtsF+c3GyAbg1ltvHaZ7ttttFhdDP9Xf+I3f4P3vf//6ancArLVcc801LC8v86hHPeqAcmVZpoE1G4TAY/phHoCQ4QOz2gC0m/tLgOkH+djMjriijyqfmCy6/XLYxbYDbAfm9rp2WOlu2+1vvuZQIxuAo48+elgJfPzxxw934N/97nf32yBuPfna177G9PQ0RVHwghe8gGuvvZZTTz31gPI7d+5kbm5u+HXcccdtqH5bCYlCEAq/rA1dQFdXAtfN/ZJwEpBjMFJbjZjyMKr8VmfUJMnYSZVaKjKazYoI7kpNSGXWhNdChOdZI7/ZGNkAPO5xj+Mf/uEfALj44ot5+ctfzhOf+ESe+cxn8vSnP33dFVzNgx/8YL785S/zr//6r7zwhS/kwgsv5D/+4z8OKH/55ZczPz8//Lrttts2VL+thJCCrAi7o2XCXI2SsFMqm9fLhOdZEeRjk49YxzOq/FZn1F9X7F+vyDNmCZls+FDE2Ce8f/s0vaB8eD7byG82Rt7jXHXVVbhmvN8ll1zCjh07uPHGG3na057Gb/7mb667gqvJ85wHPvCBADziEY/gpptu4o/+6I9417vetV/5oij2qFhOrB9eCooc2mXYRVSERb9mpRWEomkFkQf52FQjhiFGld/qTNqAle2dFofNwvxC0EUQTiWDoUY1IUO0Bg6bDfKbjZEMgDGGN73pTTz3uc/l/ve/PwDPetazeNaznrUhyt0bzjnKcvP55SYBvao14uC71Wmgg/tuP/Kx8CMmcYwqv9UZdXmMvZxOtdtMTYNfCNXqeT1wV660OW9lsFzD1HSQ32yM9KnUWvPf//t/x5j7fmt0+eWXc8MNN/C9732Pr33ta1x++eV86lOf4oILLrjPdUmAsCH3c5Gw4Cuaasnmqpr7i4RvhI0fBK5GTOIYVX6rM+rM9Ngz1nOdMzsdAr5Zcxyx7NnKPMvC89npIL/ZGNkF9PjHP55Pf/rTnHjiiRugzoG58847ec5znsPtt9/O3Nwcp512Gh/72Md44hOfeJ/qkQhIJXF+JVjmWOkJ5JqvQVDN+SAfm1G9UGPgtZooRg2Rxg6pZlqxY7bDbNGlb1c2MX3C6cQQEhlmC9gx2yHTsTVef0Y2AOeeey6vfvWr+drXvsYjHvGIfXoCPe1pT1s35Vbzv/7X/9qQPzexNipnh8VfPVbcPquvPVaKwioXvxCsP6JLZ1T5rc6kBYErL+h5z7bDwQmo/gtU09ZESZhxcOQRID30vKfym29HMLIB+K3f+i0A3va2t+3zLM0E3kJYjzXBhzhY9Adun8E+ydPMBTZBPja9DZbf6kyaC0h6g3aGXIEoBDvanqwDpYNCQt2FdlvgS492Buk3X1bAyAZgkAGU2NqU1lCWIVg2DcyzZzfQvLm/GyjLIB+bUfdvm2+/t7GM6uSL7RR01tE3FkdwB7VnDLoNykEmw0AjqRV1aYKc3XxrX+z/B4kJReKwhB2EZ6V4ZvXVN9/bRj42k7ZATRrtESu7RpVfb7zUKBFiWUpKtG6ygETT3kSH+86DEhIvN19lYHqPJ9aE9xIh9gz8ylVfrrnfJ1RTeh//rTZpQcpJIx8xr3NU+fVG4VFaI4Bev6I2IBQgwrU24b4AlNaoMWhnst7E/1QmJhKVa3IddviDN5Fb9UVzXwO5DvKxaY/o0xlVfqtz5NzGyq83znukd2Qa8ixsaKwNMStrw8YlzySZBukdboNb3cQgGYDEmtDeh4Wd4OoZ9AIa5FHXrASEcx3kY2NHLD0dVX69mbTeOqOOzI09YjfLNSKTGA9F0aZdhMKvdqu5FuG+8SAySTYGm5j1JhmAxJqQUpGp8AYaGIFB2bxlZfGXEOTGoJGWHDGtc1T59WbUJiaxm560R0zrGVV+vfHO00KTK7DGIAToTKMyjc50OBGYkCXUQuNd/E3MenNIJm2UNsqzs7NrViYxOeg8Q2ZhoTespIIOvCar78ssyMfGru5TcajyETkc2DWifEzUiHH+UeXXGykkSmtarQyXaWpR4pzBNHUACFCZJsOhtEaKzbdfPiQDsG3bNoQ4tE9DqgPYGnjjyNRKsHfQO2Xg9++xEhzOVJCPjVzdnOhQ5SOyfYPl1xs5oo0fVX69EUKSSU2eKcg0LgORabxpMoCcoZWFPLZMasRWNQDXX3/98Pvvfe97vPrVr+aiiy4aDmP53Oc+x/ve9z527ty5MVomxg4hoVcHt8O0gOWmLYQjXDvArIAlH+TG4bMjRixFGFV+vZm0GcZFkdM0UR5BPh5KCYpMIT0ooWgViqxo08+hJaCmSyYU1kORKZTafFkBh2QAHvOYxwy//73f+z3e9ra3cf755w/vPe1pT+OhD30oV111FRdeeOH6a5kYOyprcM0sYJ2BqlZGKA6WAZ2Br0IvoGoMCsFUm5HKe1XkIOWkGQA54ul/VPn1RgmJ1pIiyylaBaouQCm8h0xAlhW0WgWlM2gtUeOwi1lnRv4Xfe5zn+OMM87Y5/4ZZ5zBF77whXVRKjH+SC/Ah2pfa8OCv0wYCLNMeG1tM1jdN/KR2bFjY+XXm1EbncdujN4acfbGqPLrjhJ0sox2psikpNCaTpbTznI6WU6hNZmUtDNFJ8tgE54ARjYAxx13HO9+97v3uf+e97wnjVzcQlTOo2hmpTajHweN3xzNdDAbnqtGPjaTVgg2aQNWKtwhLyiykY+Jlooiz5ltd2hrTa4FmQz5/5mEXAvaWjPb7lDk+aYcCTlyYuv//J//k1/91V/luuuu45GPfCQAX/jCF/j2t7/Nhz/84XVXMDGedDKNFCHts0VY+AeZP4O0UEF4LkWQj83MiC6dUeW3Om2pmaKZAXEvTDXyMXEGMhRTWUYfQa0UVkmUB69Ae0WmJC2VkaFw8b2Y687IJ4CnPOUpfOtb3+K8887jnnvu4Z577uG8887jW9/6Fk95ylM2QsfEGFKZmm4JCwR3z6AtdMlKG+iK8LxbBvnYuBE3cKPKrzdT9y7yE8mvN6WSw1PTgbo8DO6rRj4mRUvRamk8kOuMmSLnsFbBtnbBYa2CmSIn1xkeaLU0RSudAIDgBnrTm9603rokJohMSpQMC/7SAWSWCM+VDPKxGVWF2CpPWiHY9iwfdoA90FI5uD/dyMfEI9AItJRorejJLDixRFPIKDPaWmGcRCPwm7A/7Jre4p/5zGd49rOfzaMf/Wh+8IMfAPCXf/mXfPazn11X5RLjy7KxdHt71lW5va4Qnnd7QT42ve7Gyq83cyOu6KPKrzdZnjMoAz3QUjm4P9vIx0QJQdHSSCGorUdLSaEzpnRGoTO0lNTWIxs5dYi1UJPEyAbgwx/+ME960pNot9t86UtfGg5ln5+fT6eCLUTZ69El7DoHuzq711U1z7uNfGxG9ULF9lp1RoxBjCq/3rTaLTrTKx1hYWXBH1wHzzrTQT4uAomklWW0tKbQmkIpMq0olKLQmpbWtLIMORwVv7kY2QD8/u//PldeeSXvfve7ybKVvIOzzz6bL33pS+uqXGJ8Mbamy0rQF/YcCENz3xAMgLHxYwDzyxsrv94UIy7oo8qvNxmQ73UKmSXEJvZuEJMX8bOWrPdY75lqFWzr5ORKh/eyb5oYKs22Ts5UqxjKbjZGjgF885vf5Bd+4Rf2uT83N8fu3bvXQ6fEBFB7SUnY7Q/2RXu7gETzvGzkYzNpE8HmpjdWfr3pO4+r4HggVyENuE9IBsgJi83RCioLrgryMdESMiVBQ42glWvQmrxpBYExOCHItCRTEh3/LbzujPxPOvroo7nlllv2uf/Zz36Wk046aV2USow/BStDvQdpf3sf9wf328QPUALoEZUYVX69kSO6yEeVX2+EdwgVMn2kCov+DKEtyAxNr6jmuVBBPibWCQQC5z25lug8I8s0Wa7JMo3OM3Itcd4jEFgXe0uw/oxsAJ7//Ofz0pe+lH/9139FCMEPf/hD/vqv/5rLLruMF77whRuhY2IMkbna5/jo97oO0I18bIoRVRhVfr2ZGtGlM6r8euMRZDqc+mwVMn1mgcOa63Rz3wKZJnpWTZ6F/j9CBL9/noW8/zyTzTXcF02foDyL/x5eb0Z2Ab361a/GOcfjH/94ut0uv/ALv0BRFFx22WW8+MUv3ggdE2NIprPhrv7eMj6KRj42nRF39KPKrzeL8xsrv94oramatiCHz4JdCH7+wcyIaWB2Fu5aCG4gpSMXgjlHISXt3FMZhxKgtEI07aCtcRjjaOeeQkqci91taf0Z+f+AEILXvOY1/PZv/za33HILS0tLnHrqqUxPR3ZAJu5TMicOuVWCauRjM+p6E3l9YtSYY+wYpTaGTDSV4CGdnqxpEZtJMCbcV4Rma9rELa3VWpNliqLVZi6TLPdKjHM4G9r+FJliql3QrzOyTKBjvyE2gJFdQM997nNZXFwkz3NOPfVUzjrrLKanp1leXua5z33uRuiYGEMq5ZGsxAEORJum74uKn0ExaTGA5RE3nKPKrzc+UxQ5TAnwppkS50NSgG06x3oTnhd5kI+qryekfypPZTyFFsy2M2anMmbbGYUW4b7ytLSObmA3gpENwPve9z56+8np7vV6/MVf/MW6KJUYf3KpkFlo+3Cwqs8eYfBHPgaNtOoRF8hR5debYsS/f1T59UbpFq3pEIzO82D8Z6ZhqhOubcJ9mUNrOshH1VcJ8kwy3c5p5xKlFK6p+HUIlFK08/A8z+TWnQcAYSyk9x7vPYuLi7RaK//zrLX83//7fznyyCM3RMnE+FFbh6mDj7dNyPUfDNwaXNs0w+LrIB+b9ogn+FHl15tROyVE7qyA9BYpYHYGZAfqxZD1I324FllY+HMZGgRKH7s6XAyre6dbGXUdWhhWNqSxgiDLMoy1jdwWNgCDsZBCCE4++eR9ngsheMMb3rCuyiXGGGcYeHBXNwAb5Hx3V903jXxs1IgunVHl15t8xLDaqPLrTcirh+lp0C1J3XE4EVI+EdDqwGxHYmQYJxo7r17KkONfu5CPJKXEE2JbQjLs/qOVJNMSKbewAbj++uvx3vO4xz2OD3/4wxx22GHDZ3mec8IJJ3DsscduiJKJ8UN6Nczw0IRMnw7hRJARUv108zxr5GNTjLijH1V+vTl6JiOcoUaRj0fR6rB9tsM9u7tMtSSdtqPoQOUhF1B2wSPp9x33O7JD0epE1RfC4q6lpVc7cA6xylXpnKPC0c4EOnLn0o3ikN/ig7GQ3/3udzn++OMPeUh8YnNSaYEkpPZpQoCvZmUirG/uT9MEgfUYvF9G9ThE9lCYEbfIo8qvN0prpvIOrU4X52wYDiRB2HD1gHOWVgem8k70NFAQIbArJEXmKUuwzlHXILLwvi2aCTEhADwG7+F1ZuR3zD//8z/zoQ99aJ/7//t//2/e9773rYtSifFnSgpaxUqF78D3r1iJAUB43iqCfGxkNpoOo8qvN4fl2SHv0HQjHxPpLZ1Mcvh0RjvLURKEa4YDNbn17Szn8OmMTibHIAYQYprOOYzxCCFQQpLlYf6vEAJjwnPvByOPNhcjG4CdO3dy+OGH73P/yCOPTN1AtxBeaqQO/v3BsmNogr7N66z5XuogH5u8GC1KOqr8erNoHYeqQd7Ix0QJjRASpQuO2XEYRx+WccS2KQ6bneKIbVMcfVjGMTsOQ+kiyIm47wnvPcY6jHV4EeKYUoXAsFQh3ukFKzKbMA905P8Dt956Kw94wAP2uX/CCSdw6623rotSifFnutViugN6OXhKDE3TN1ZGQw7iANOdIB+bYkQXyajy602mcxSHNpRANfIx8VKidUYnz5nKcrK5WVSW03fQkmDrijzLcXmO1hk+8sQd76EyDusFuRLUTmCtDzULHqQQZFJQGUFlXKoDgLDT/+pXv7rP/a985Svs2LFjXZTaHzt37uTMM89kZmaGI488kl/+5V/mm9/85ob9fYl7wTvyLIx8vKe51aJx+TSv7yE8z7MgHxtbVvcu9BPIrzfemuHp6kDOndXPvY2baaWEZ7qVc+zsdMiekQIpFZlSSKnQUiCAY2enmW7lKBF3RXXOYb3HO8tyGdxRmVa0mh5AAMulxTuLbVxFm42RDcD555/PS17yEq6//nqstVhr+ed//mde+tKX8qxnPWsjdATg05/+NJdccgmf//zn+fjHP05d1/ziL/4iy8uRm7ZvUYTSeBt2+HOEDKAWIfe/1byeowkQ2yAfm6oezec8qvx6U+TZMJX2QPkyg/uqkY+KUBRSM9Nuc+RMh+k8p1Bh7GKhBNN5zpEzHWbabQqpm/zQuNQ2ZP50coX3nro29CpDXRu893RyhZBqLOpYNoKRP5VvfOMb+d73vsfjH//4YW8M5xzPec5zNjQG8NGPfnSP11dffTVHHnkkN998837nEyQ2ln6/z8JSWPC3tcH0wsJvCG8qter+wlKQj83yiDHdUeXXm7mZaY5kFz9m5YM6S3CzZYTT1eD+kY18TIosY2aqYPdCSZFniFYBStOz0FaANeQqo6xKts0WFFlcg+WcxzmPViEWUNYW66G2oZ5BCRBCIvDYRnazMbIByPOcD3zgA7zxjW/kK1/5Cu12m4c+9KGccMIJG6HfAZmfD60PV9cj7E1ZlsORlRCqmRPrQ9nvU1s4XIfy/qy3kg6qaaZD5XB4HT5Q5RgYADfiULJR5dcb5yBvQdbfc9iOZ8/XGUEutodCa8G2Tk5/uaQ0But9M0qx0dl7KmNoKdjWydGRU4OVEmRKstSvqZwglx6lNTJkfmKNYVe3Jpee6Va2tVtB7M3JJ5+834rg+wLnHC972cs4++yzechDHnJAuZ07d6bq5A3CZhlKB/++arKBBHsGgXMNqh0agdnIuz2Azogeh1Hl15upIqPdgaP7wVe7i5VA++C3Oeiz3+4E+ZgopZkqMpTStISkZ8DhcDRpwgJamUJ6MZSLifdhGEy/drTyDIHDOx909SErKFeSflXTKTzeb1EDcOmll/LGN76RqakpLr300oPKvu1tb1sXxQ7GJZdcwte//nU++9nPHlTu8ssv30PfhYUFjjvuuI1Wb0uQZ5pOC+7ZDZSh9cOg948nNIFbWAQcHLYtyMdmerpN0GwU+Xj0raDIgp/fEQLsU6wYgGWaKVuEPjt9G3eBklJSFJowZ0vS0gqpM7SElgInHILgUikKjYycBSREyO3PtURrgTXg8FgLqLCh0VqQOwn4Rn5zcUifyn/7t3+jruvh9wfivqgOftGLXsRHPvIRbrjhBu5///sfVLYoCopiHIYRbj6mhSRX8GMTFv6B73/gAjLAUhWW26NVkI/NzNQUh9EbZi0djMMa+ZjMtHMG3RLaUzC7HAzAoN+Sau6Xy6HPzkw7bhqotRZjHDPTOd3SUxmF86Jx/9BM3pJ0CoUxDmstWcSNgW9GPRZaUFuPEiCVRDXN6py1GBvaRAvE1q0DuP766/f7/X2J954Xv/jFXHvttXzqU5/aby1C4r6jaLcQIuxCt2vIzUrnBEtYoFoa7jLBn1q049cBeMICeigGYIr4dZ+1NWDCaMo8Czq1RHBP5CIomGcES2Aa+Yh47+nXFq0107lg2fSxoikWVKCkYmomR7ogF3tB9V7ihEDpjJYU1NYO4yjCh5TQjlLUzuOEx/v4m5j1Jv65/BC55JJL+Ju/+Rv+/u//npmZGe644w4A5ubmaLcjD0Pdghhr0VlY6Psm+KYLwu60ILzum/BcZ0E+NpnStGh85sCP9yNzBOHU0mrkY1I01da+GaAyLaGYhryZsKWXwv1ShGrrInK1tXWesnJUtcVLidQZUim8aSaDWUtdO4RzSBHkYyKERyMQKsSplPBkmcRY0CrEGh2hNbR3Yuu6gH7lV37lkP/Av/3bv12zMgfjne98JwCPfexj97j/3ve+l4suumhD/s7EgSnrmtrD0QT/f4/wZioJJ4CBp/1ooPZBPjpS0lYwa4Nh2kUwBL1V1ymaGQcqyMfECRmMbBaG6mybCwbANn11ShWaluVZMLIutpvNe4xwlP0aVEamNSrLwilFgK1rqtKBrdGFiD7DUoiQwKClpDaenvWY2lIPDKwM74NMS4xzbMb+l4dkAObm5obfe++59tprmZub44wzzgDg5ptvZvfu3SMZilGJfVxM7MlSbVleCqmeQsJsfyUGkBGyU6ZakDlYXgrysekUOVMzsLA7uCRm7ErhWkaTvqoAC1MzQT4mFoeUcNTRIFuKxcpiDVQuDFVRGmZmFTO5RcogHxOPp+4bPIKpToYzBrwLu2tAKUlWaJYXTSMX9zMtpSKXjl3dCq0VSoa2zwOvmveOynm6XcP2To4cg6l2680hGYD3vve9w+9f9apX8YxnPIMrr7wSpcIvxFrLb/3WbzE7O7sxWibGDm0NZQ92V7BjDnb0oZVB6aCQ0K9DF9C750NATUf2TwPkOmeqA3I3tHOY7gUDMHBfKcJ92wtjDPPIvXVyKWgXmjwXWJFR6S6iIMQFdDC2rbxAZTVKePLIHVe983gJeRayZpzzeMFKVo33SDx5JvHS4yO7gKQMdQDGhn5AhQpGShIygIxxlLXDO0emNudAmJHPjH/+53/OZZddNlz8AZRSXHrppfz5n//5uiqXGF+sM9RVcPd4mh2TCB8cL5rXhOd1FeRjUxSazjS0JRStsOjnMriDchleF63wvDMd5GMis4LZdgdjHEiBLiAvoNUKV10AUmCMY7bdQWZxM96kkhQ61CL0+6GHTthVC5QUeGfp98NJsNAZMvKQldALyDHXyejkEus83dKw2DN0S4N1nk4umetkWO9SLyAAYwzf+MY39rn/jW98Y1P+ghL7JysK2q2wwPf7wX9eVlDacO019y3QbgX52GilmCkkc9PBfQJNr3pWMn6UhrlpmCkkWsU98s+2WmyfmcJYi/chMKkzTZZrdKbRKrjRjbVsn5liNnrHVYnWEp1pikKGbpu1pV/aEBj2UBThudaSNSw/647zoX4hzxTtXDHdUkwX4drOFXmmkFKyCbtAAGvIArr44ot53vOex3e+8x3OOussAP71X/+VN7/5zVx88cXrrmBiPBEoWtPge7DUFIIN6lA94TVl46aYppm0GhePQrUKslZvZYCNDJlLSoJ0kAmQLVCtAh9ZZ4FnVmtyEfzrNNWp4Uq44skFzGqNiOxT1wo6WlKVBu81mRYIpTA6BFS99eAFAktHBwMWE+dC9s9g1rlQYQiMFOH94H0YEuMJcs5B5D3BujOyAXjrW9/K0UcfzR/+4R9y++23A3DMMcfw27/927ziFa9YdwUT44mUIVd6G2F4+mIZagIGRUoWmC3AlkEuckINAFoJci+DLhlMiZBVkw2af82DLEDWkHuJjtz7JcsydCtjamqaomizu+yCkqH/j4KsgG2dKUol0a2MLHK7jbCTluFaKEyl8VLiTThZCanRuaIq3VAuLiEOUeSK2qxqBtdkASkBrVySa0lZDZydm4uRDYCUkle+8pW88pWvHDZXS8HfrUcmBEo3gdMCsjIs/I6Va7uAXhk+/NkY5NBZYylNhRTBLVVNB186NhRbiemQuVQaKE2FNXEzl2rn0V6zrSiolGI6z8laHboOOhJq71FKsa0o0F5TR/dTSHKt0cogvSBTAiEkMgsnKy88wodMm1xrYruAhAgbk15ZUzlw1g13Kp4wCaxbW3Jr0UpuyjTQNf0fMMbwiU98gve///3D9g8//OEPWVpaWlflEuOLUhmtLPj6u02Kv6Yp/GpkunVTVJUF+dgIAOcQEsTABdEc9xHBhSG0RshGLqq2oRVB7RzHbJtjtiiYarVRSpMpHRqvtdrMFgXHbJujdg4XudhOSsi1YttUjhDBPTV0Va16vW0qJ9cq+qlQiDCpftdyjbEehEQIiZbhipAY69m1XDf/htgnlvVn5BPA97//fZ785Cdz6623UpYlT3ziE5mZmeEtb3kLZVly5ZVXboSeiTFDaoXOwwnAltBn2JEgBIab+wrQeZCPjfEepxTeW3pdgzEh+wcPSDAGel2D8OCUwkRvVeBx1iK85MipKZa8wSvFsoMpCcLmTE9NUVclzsZvrWCtx3nPdEujlGNpucbgqF0TwJaC6Y6mnUmc91jr0VETrTyeZii89eQqjIEcWH4LGNsMhScNhQfgpS99KWeccQa7du3aowXD05/+dD75yU+uq3KJ8cU5w2BIkvMhALx6IljW3IdQuerGIA0UKfDWI2XY9esiBPWUDlddNMHgQcAyct538JELDA4tJe1Wi05eMFsUdPKCdquFlhKDA0R0n7rWEiUFvdqjhKRdZEy3Muba4douMpSQ4bkUTSZQPJxz1MYx085oNboY46iMC6m3QEtLZtoZtdmcaaAj29/PfOYz3HjjjeT5nkUyJ554Ij/4wQ/WTbHEeOOdp+6FPdGOw4C7Q///QavizMBhh8HddxPkovunwdeWytRYD9OdHFFV6BwqG/q9aAetTs7S7orK1PjI1ctaSWZaOXcvdJnvlwhbo7ICHBgZWiv0ncQby7bZDjp6Xn1w8VRVhROaXAoyrdGNUTXGsFxZpDdMFS2c81GzapwD40OrcikExoa02kGrDSEEWimcl1TGRh+4sxGMbACcC21c9+a//uu/mJmZWRelEuNPv6rp1qFyNstCEFXnIZVSNRlCWRaed+sgHxshw0G+UKEmQDU9auQgFpCF+4Vq6gNk5EpVoWjlWbPL9xhrqIXCuJBWiTUolaOlpJVnyMgzdqUUOOtDd00l6JWWnqmHIxa1gE4hqW2Gs34sKmsFoUjNeZpAr8A3MQvvw/tFydAOejMy8pbhF3/xF3n7298+fC2EYGlpiSuuuIKnPOUp66lbYqzxFCoEeOtmCoxupoPpLLyu6/C8UEE+NkJppooCocAYixCQ56GwKs81QjT3FY1c3EpgpSVFrtBK0dKaXClyKVFSkktJ3tzXSlHkChXZpWKtwwvAGZZKi8DTyhSdQtHKFALPUmnBmaZFRNwttWgSAKQI8QkIp5jV83+1XKkL2IxZQGuqA3jyk5/MqaeeSr/f59d//df59re/zeGHH8773//+jdAxMYZ4qfE65KKrArpNELU2hIwJGVIsrQevg3xslNTkeUGW16hMYXshDCgc+MEEqEwhrCTPC1Rsnb1HCkFLK7wM1b9Ca1TTb8kbh1IaIVwIXkbvrikwxoPUdJQIw2B8s3f2oLUmFxLrJcb4+2SA1MFQSlIoSWUc+eokhb3UqoyjUBIV2cW2EYz8Dj/uuOP4yle+wgc+8AG+8pWvsLS0xPOe9zwuuOCC1Jd/C1EoQUvDfL9xAylotRm6J/om+Fj7/dBaoRiDgdoKz7RSVO0Mo3K87iGkbI75ILWlpXN02zGtFCr6qSW4HqZabUQmWVrUmGad9wK00kxPZfhaNS6KuL9jax3GO5QArdWwctk16fXeNzsDYzDeY60jZu2alJJ2oVle7GMcZDLUVYjGBWStpTQe7yxzM63oQfaNYCQDUNc1p5xyCh/5yEe44IILuOCCCzZKr8SYM1XktNrBh5gXIe3T2BBQHcxTzYvQabPVDvKxKYqMTidnvlcz22mzu1xGZhnLFqZUmAe7rdNmecnS6eQUkYese0BphRSGsvS0tEbqfNhawZmKqvQUyqN0fHM1qA7XWqGUwDnZxFIIsRYhkFIgUBhjx6AOIBSltXKNMR5jHXUz2U4BQjiUUOhcD+MDm42RDECWZfT7/Y3SJTFBOKHptDRSGkwZKn5bKvSqlxL6JXTK8H2npXEivgtIqozpdocs64UWAJlG5yGrpi3B4PAuTIWabneQkYvXpBDN0BLNdK5wFpAK2UysQgukynAuxDNk5AXKe4HSMgRPmz5FUojQd2kQVPVhtm6Qi62vBwGtTFIKHyqBm2wg3bS3DR1OQ23AoDfQZmJkG3zJJZfwlre8BWPGIK87EY2pImPb7DR50zqhRwj62jJce4T7eQu2zU4zFXk3DWGy0/aixWxe4ARoBFpp2kqjlUYjcAJm84LtRYssclDV4xHek2nB1FRGq5VRFIpWrsK1lTE1lYWma95HH7ASfOqhe6Zvgqm18dQ2XK0LvXekDHLj4FO3Nrh9pgpNu8hoZYp2E7RuFxlThUYphbWxz1cbw8jbsptuuolPfvKT/NM//RMPfehDmZqa2uP5Ro2ETIwXy7Wh7Nd0cvB1yP9fqpvhKs3rTEAng7Jfs1zH3zB4G9wqh01PMY1iyVVYKTAiDFlvt1tMT0+TY0PdZ+QhZloFH7X3DknTW0dKahmalXkX7mutaBcyeh2AEFBkElvWVF6gCDv9QQzAWYfxnlx4iiyLnlXjnMf6UJQmhCBr0mgHaaBAkxa6khmkxiCWtZ6MbAC2bdvGr/7qr26ELokJopBQ2Zrdi1B0YA6YnoZe405ZWgof+t2LcLStKeJv9pBKkElJJjWdoiB306A1XQMdDRhDp92iLksyKZGRP+zeS1o6w+eGyjnq2uClwhiwGoSzZDL0rW9pjfdxf8lSSpRwCKmYyST9qsI5S1U3c40FdHJNZR1KxK9cXulTJJrXYuX+HnIh8h7bYG0EIxuA1eMhE1uXXlWzsFhRVdCZg5lpyDtNT30ZgsAqg6qChcWK3jgUggmBzjVFZvBe0M4zlM5DIZsAKwXCC4osBP5i+3u1lnQKTW0dfWdDvx+Cj1pKENbi8Gil6BQ6emuFgc7aWPqVxViH9xInwBpwwtGvLHkWvw0ENFmzh+DbH8QKNuNY8kP+v+Cc4y1veQtnn302Z555Jq9+9avp9XobqVtijOmXFd0eFPkgxS/UAZg6XH0zA6DIodsL8rHxXqClpJMXFLnEI+i7MPe17xweQZGH51rGD1IKEbJ9rLVIHwLCEkCFqxAgvcBaix6TQqWBy6R0LhjcTFFoFeorhKB0bmyCqVIKlBD3Ou3LeZoTS3yd15tDNgB/8Ad/wO/8zu8wPT3N/e53P/7oj/6ISy65ZCN1S4wxzlmMDbv8ugqLfFk1vfSb13UVnhsb5GOjlaDIFd47nA0FVrN5zlwrZzbPaWmFswLvXVOBG9sANIup8WgtkEqDlCgfmutIpdFahFz1MVhUvYeysjgk2zs57SIj1yF2kWtBu8jY3slxhAEr47CjVkogaALUeyk08P0LNp/vf8Ahu4D+4i/+gj/7sz/jN3/zNwH4xCc+wVOf+lTe8573RPflJe57etZjHHgHtQ0NtCoTdktehNe1BeFCcVhvHLIomnF/AkGnpan7Gi9EmFgmCL32WxpTutAINPqC6nFAVRsqK2hnijzPwxzbpulat+/AhX6gsdtBO2epnSVXYbes/IpPfbB+SinIFdTO4pxARqy2Hox/lFIiAOdDoHcwJ1o0LSI8oknJ3XxG4JBX7ltvvXWPXj9PeMITEELwwx/+cEMUS4w3s7lGSdg9H15rFaZstdrhOqis3z0fqmxn8zGoAxCCrCnpH36U95oKLwjpjJmS0fPqjXEslYbZqRbbpzK01hgX0iqN82it2T6VMTvVYqk0wxbGMXE2LKjGOmobTi+VCdfahmIrKSVjcCAEgkGSohkJIcIpUUuBVnve34zuHxjhBGCModVq7XEvyzLqOn5wL3HfY6xDuKbnvx/MfAXZpNApHe4735wCIjf+GpBphRCefmWReHSm8E3hj60M/cpSKE82BgNsrLX0S8tsJw/pn/VgVGEzvQzIMo13joVutd8uvfclzgm8FHRLg0egJXukpjrn6NeucalInBuPRVUriXM+nF59sxdosoM2q+9/wCEbAO89F110EUVRDO/1+31e8IIX7FELkOoAtgadVkGrBVqHts+1CjulQdWnVOG+1tBqBfnYeC8wtSfLczIl6C1WjXsluLK0EExPZ2A9pvbRg8AQ3GnBVSGGRkmrlVk1UgicEIyBqigFwvumtbLYbzqlkmHKlvZxZwHsjZTBFQj71gFsZg7ZAFx44YX73Hv2s5+9rsokJofaQacD22bCIoUNi71vmsFVNtzfNhPk6rE4ADhQHoHD1MHFU8iwChUqDK2xtUfLIBfMWTyEkGRy0K1S7LFDHS5SzlMZRzaYYxsR5xzeOzJJ02N//z514UPA1TmHGiMrcKA6gM3MIRuAlP+fWI3OFCrPOOpYqKyhd7enuwylHfT/h22zgny7bmYEjMcH3RmPMQ4vZUheUBLlQSgQhOZlxjiciR+0zjLJTFuz0K0xLkwtW71gWmvpW3DWMNvJyLK4BkAphZaOvvVkB9lRl0bSUmKsFv+tSvzIXGIi0UJwWKvg9nuWQEimO568A30HLQlVF3ACZ2sOm55Gj8G2ynuBwVNbKDKBMXK4qw6ZNRKlBGUNhvguICklba1YlgatQnqqsW6YBYT3ZEpivKCtVfRsPOeCoc8x9Co7NFiD//XW2pXxm5katohIxCMZgMSaaLda5HlGr4LOXMHMbA/VEmgHuYRCe7JWQXe+R55ntPdKIIiDC73evaXfl2jhyTOFVMFtZSpLv2+R3iJ8RmwXkJShVXErt0gpscaC8E1hEtD02tFS08p1dAMgZUgrnG7nVJWlspaqtthGXyVhOlfkucIYlxb/MSAZgMSakFLQ0ppMNXnUEkSTUidEmKcrpQyDYrQei0wKD9TWkRc5MlNUXUtZGWoDmQaFoDOlcbWgti56f32ALFPMdTKW+k2kmjBgJaSuOgQw18nIxsTFJmWIBeS5QjVJSdYyDPgqpXAuLf7jQjIAiTUhvKfIM7ZN5/RwWEvjogj+9JDn7dg2nVPkGWIMyj69a6prncU7hdaSTCmyLOxOvbV4F7psCqHCehtT3ybgOxhYsmwExvuQBiqDG66lwylhj8BwJKSUzTQ4Ry5X4hV7u/qNg5aW0U8siWQAEmvEOIn3ik7RRgsJ7R6qyOgbaGmwvmYmb5F7h/cK4+J/2JUKhWCVcHgfCr6kVkg/SGH1WA8MC8Zit4IImUndyuBlaFLXkpI8C35079ww776Ta8QYtCvQWqKdozIOLdljkXfODUeGjkMzuMQaBsLE5IYbbuC8887j2GOPRQjB3/3d38VWaesiQhpfK8+Ya7fpaGg3FZRtKehomGu3aeUZzjmGKSERcc0OucgVnSZVyRhLVVmMCf6KTqEo8hC4vLcmYRuN957aWMra4qzHITEuDFo3TuCQOOspa0ttbPRWEANauabVLPDGBGMwqFIenFgS48FEGYDl5WVOP/103vGOd8RWJeEMzhoKKTC2ZrmG+bKm16uZL8NrY2sKKXDWgIs/ECbTMuyUmw6QUoRpW1oJMi2QTYBVCEEn1/EngnlPbR1lbSmNawzSoDI1/BtKE57X1o2NAYCww8+1pJXLZtEPr9POf7yYKFN87rnncu6558ZWIwFIqVFa8e07fwz5FFoL8s4MeZMFVHUX+dFyH6plTjn26KhNvwZ4L8i1QktD6cP8X2RIX7QecD50ApWQaxU9DdQ5T1mHhT8UUu3ZlsA5MUxhLWvXTKyKp+/eDNw/k+LqHxjQVAm8SSjLkrIsh68XFhYiarO5UFpj8XT7ju0djfIFbZ2R+zAKUuYFVmt2LTgsHqXjv9WUkmFXLyQ5glqGBdTL4KESUpCJ8CLTMvrMWmsdfWMpMoWSEusc1rrQboPGnaUlVkLfWKx1ZPFHLw9xzjXXFSMwroHf0AvIDwPvg8rlzdwIDibMBTQqO3fuZG5ubvh13HHHxVZp09Dvdyn7fY6cKWgrQb+u2d3vsavXY3e/R7+uaSvBkTMFZb9Pv9+NrTLgQ9tn4REqBFU7Lc1Urum0NEWmEEqghG967cR2qTRzaEMPa3wz+N3ZcB1MqlJS4AZd+caEge+/Xzn6zXV1LGCcMNZhmoCPlAIlV05axvmxaWS4EWxqA3D55ZczPz8//Lrttttiq7RpEB6MF0y3ptAqo1CKvGm1nCtJoRRaZUy3pjBejEMMGO/BGk+eaWZamiLPUDL4pZWUFHnGTEuTZxpr/BgMLAkLUVlbqjpUAVvrsXhs01q5qkMMYBAXGAf6laHfLPSDWMDA9x+MQfx40IBBF9DBYPjVhOZ1IdbiYmcEbBDxz+UbSFEUe3QvTawfldBIoem7mlahkUUbnRd0fJiva6RCa02/ruiIjEqMyVtNCrQKC77KQi76IDXRDXwVatBrIS5KhcVzsWeQyuOtBSGHlbV419RcOGbaOrrLCsLO3zjI9xPslVKG+FBzEogdEPY+uH3u7X+1FGFYjPCbLyYwJp/KxKRRyDCmcNpnWELnStvsQAevBTCdZ0gtKGT8HZRzYVc/3dJUxlN7MCZMNnMyfNAzAe2WpjIu+q5PCMikwBiDtQqNQ2oZAr8iLPzGgvKWTGbRu1iGPP+Q/38wtATjHNLFjwmE2dWi+X7/QWAhRPT3wkYxUQZgaWmJW265Zfj6u9/9Ll/+8pc57LDDOP744yNqtvWQ3tOWgq6ryfMWFocUDtPsTh2OXClc3aMtc2R8f8rQv+v8Kl8vwb+uZXCgyOb5aj9wLIQQTTRS0s5WKpN9MxxeKkkmFVUdYgHjsDt1bqXI60ALqpRyLGIBw+Ev3HsQeBwqrTeCiTIAX/ziFznnnHOGry+99FIgzCq4+uqrI2m1NWm1p5ia6nB3z3KYUtRaE8Znhw+P1ppCKe5BccRUh1Z76t7+yA1HiDDub7k0aK3Jmg6aA52dc1gfZvBOFTr6guqcxzlo5wqpJN46hBRDl5V3AqEkSoTOmrHTQJ1baZ93bwuqg+jdQAf2dXWH1dVG3/swelN638wEjqfrRjFRBuCxj33sWBW7bGWkFBw5Pc0P77yb25eWaJs+MzPbUU0MYHFxF7cvSaZ8xZHT09F30wOdw6SqYAh8M7BkkFYJYWCJFc1M2Mg6+2a61mwnpzSO2jUByWH7akGmBEWRU5v4hWCDbqCHtKAyHvUB3nus83uMrhwQRkI2408ljEuQfT2ZKAOQGB8GaZLH7ziCeVPz47uWuGNpgW4NnQwK5zh2tsOcnmOQfjkOaClpZRLrPa4Jqg5m7OIdUglamUSPweq02kWhpURoCaycAMCjGj0HLor4eIzd/0zlwYJaG4selzfEFicZgMSasM7jbaicnSpamE4LqzKWDExrUFYxVbTwpsbbsMuKT/A/Z1rhjAsjDAXUzeB64R1KM3QNxd7xyaa9dm3D4BdQeA+6caWIpgagtm4sCpYGs4vd0Pe//xjA4PQS28U20EfJ8H6We8VRBiew/aWIbhaSAUisCa0lMpeUNjR6m2lPofMWM81ISFP1MU2uuszHswdMCLKummEwZkd8ISBTodun8xotQ2roYG/tnAs9gqyhU+ix8FErJcm8p19ZpPAIsRJj8d7hvCDXYixSVgcnLK3kMGaxvxnGUoqw4UlB4EQiUNaWqmfIlcQLQYXAGkc5LEgV5CIEKaueoawt05F1DpW1jtpYhJBoJRFSIhsX0GBeQG0smRTErpOUUoZW20KilYC9FigArQSVC3LjkFK5sqA6rHM477AuzFsQeLQMv/dxyKoZBIEhnJ4ONMMYVgzCZiMZgMSaUHhqHEoqhFZ4I/FKYE1YTAXB1eKNC3Jj0qbAND3pi0xQWREqaj1gm6CqbGYCu/FIU1Ra0nKyaQjngwuFYJq8D3OLW5lENfUBMRnML3CEzqvCruyaVeNe0Sr8W6T3YzG/QDQtNsQql9TeC314HkG5+4BkABJrwrjQk8Zai5IZmc6QKkOpUEzlhMC7MAjcWTnstRKTQVqltZZF69HCI5VCN4uUtZbFvkHhcE5GT6sMaZOCqVZGVbvghgBwIGR4pqQkzySMgXti0KvI2qCglDIs+queG+ebCmY5XHhjIRr3n3Geg9ki54MraDPGAZIBSKwJKWWzQikyrbB1cD+Ixj8hhURphbUq+NnHIKsGwHgPQqKCV3q4Yx0UKisRXD8m9naaJqunCfS2coWxTT+jgUtFgFZheM3ARx0TMTD6XpDvZ0UdZAFVRqDceBSuSSmQTSrogYLA4xBg3yiSAUisCYEnlxIvQ5Oy4Rxd0fiCncNbRyEFuZRNiVhcrHVUtSVTEikktQ2BP9v4VKQQ5ErifJCL3V5ZSoESK5XJmQjHkb191NZ5lIhft+CcxzdV1QdbULUM1czhhBV/YT2UIPBmJRmAxJoQQpJnirp2SCUxVuAFsGoxDcU1QU6I+CeAwaHFOg8qLKp6mJbYLFCE50KMR6GSUgJvQyB1sKAO1tRBEZPAj0VWjQiHqpBme9AFNbSCGIMDwJB7CwJvVpIBSKyJTGtarYzlniWXCqUVQikq2wwsFx4lFZWztFoZ2RgMhPE++HGN9Qi5UmU1LLhq/hMqQ0X0iWADH7WUobHegRZUjxiLvPqBy8p7f9AFdTDHILbLam8OFATezMT/VCYmEo+nlSk6WlA3FalSawSgNDjv8Hg6WtDKFH4MXEBSCjIlMdYGFwV+Jfffr7gogOAmGoOj/8BHPcgCklIMe+iEoKsYGx/1Hi4rceAFNTzf3K6VSSEZgMSayLSi0IpWp81Upqi6Hi+h9JBrEFKTd1rY2lJotd/WAPc1gx1zpkSz9AdXzzCtklAMJpRosj4iK9ygVXCZGA/ONkHrxghowX772MRify6rAX6VIRsHl1UiGYDEGrE2GIFO4XBSBr+PUggVDADWkmlJIUNfGGtjawyDCVuZVmglqU3YjQ7SKlXTJsLYwe50PCyAaeYAq6aZ3cClMohZGOvGwghMmssqkQxAYo1kmaSda7wX9GsHWuGlaipBmw+7ELRaOe1ckWXxFygpRZhR4C3OhzYLUsphENg5N8y4yZUaCxfF6pGFA4O0d4DSurDQjoO+q11WUoCQexosz+ZOq5w0kgFIrAnngh9XCU+RKXKpEVJRW8jUoFe9QgkX/MLxC2vD9CclaEsVUkBXtVeGsKBmTZwgBCtj6zuZIwtX0ir3DLCHOoDk+x8nkgFIrAmtJVoLpNHM5YqqboJ/KuR5S6HIM02vsmgtxqIZ3DBIKQQtyUphlV/dqiCcYiTjsUtdPbLwQIzjyMKtmlY5aSQDkFgT3nsyKSmUbdoVC5RSw6O/teF+oYJc7GElAwZBSk+oUxjs9Fe7KMYlSLl6HsC9yhK/udrebMW0ykkjGYDEmhAiVNO2WwV4T2VCT5VBmwIlJdNagdDIRj42+wYp9++iGJcg5epulfcqS1poE6OTDEBizWgtEc5hnCBXYWEdTKtyzoEQoYf9OJTUNkxakHJ1t8oDsZm7VSY2lmQAEmsidKoEnSlk7SidoDIe48DJkLVSSEGWhYDgOC1QkxKkTN0qExtNMgCJNRFG6Qn6tQXC90oKVBNQhZCjXtaWVqbGbnGalCDlVu9WmdhYkgFIrInB/NfKOKSUzc5ZIpvF1DkXZgY4RyfXY7ewThJbuVtlYmNJBiCxJgY56rmWYciK88OK1RD09aEjqJY476MP/9ibwYI6cAENFtRx3U1PyoklMVkkA5BYE4NWxKpZLIUPlaoDlwQM3ETBOOgxMgDGuv26TgYTq6T3Y9FaYW9SWmVivUkGILEmvKdpqyzI9Err5P0NKzHWh/5AY8CerRX2ZDCxapxaKyQSG8mYfCwTk8bABZQNd6X7351KAXXjAorNpLZWSCQ2imQAEmti7yKlwQK/P//0OBUpTWprhcTGc7D38GYlGYDEGgnZJ4P0xAMFVJ33Y9Naee/WCgf7wI9jawXXdNQbDISBUHyX+MmZtKSA9SIZgMSaGEzX6lYGjwBnQiWwD0NKnHMgNQJPJ9dj8SFafWq5tw/8OJ1aAIxx1NaGjKum1kLK0Hl1HBrt7Y9J2VFPalLAepAMQGLNCBGCqX1j6XYrDIKygiIHjafTEbS0GquFVAiojd3vKMXBB1648RiwMqBfGSrjEbhhTyVPWLhqC7lztMYlyt7gnMc6h2/abYcittAWZBw2AwNWJwXYZmqRtaGrLYBSalMnBYzXuyYxcSz1KrqlxzuLkBqhAAe1sywsGUxhaWXt2GoCze7Th3YVuT7wB74yHi3HY7dqjKNfO2TTpG5wYgkhiqBfv3Zo6cbmJGCsw1gPw96qjb4+GAWtxFgY2NVJAVVlqazFrjphqWbQXZbJTZsUkAxAYs2UlWWxb5FK4l3jX7HgNcEtJGGxb5lphfGQsfHegwjN6nqloTYG58WwgZ0Unkx7ikzCITRh22icc1TG4pxDKIV3rkmtDYuT9x4h1VBOyvgxAec8lQl6DvVtDOxA38oEgzYOO2rvoVvWVBayZnypblxW1lqWKktuLZ0ii63qhpAMQGJN1LVhsVeBdzgbFiXvPMaDr4MtUNaDdyz2KopMkOfxP0Teh/5E3dIhhQ9trZs10zpHXVrAj0UHU+/9sNWGtY7KuH13qDq04KiMC4Yrsr7GhkC1NY7K7kdfJZBKYKwjEzKqgfU+bGIqC0VTsW7s6lYbkkILSuNQlUW19Fi5M9eD+O/yEXnHO97BiSeeSKvV4pGPfCRf+MIXYqu0JfHesWxM6P456KQpBbq5ChGqgo2DZWPwPv5MyMEHvls5ilyRaUWmmzGQOgyLL3JFt3KUlY0+EtJaT209vbJmqQzuqjxTtHNFngWf1VJp6ZU1tfVYG1fhQXV4r6xZqmwYGqQlhZZkOgwFWqqCvtbFrw1xzlJbiyT0rYJmatyq/krGeSSDALyNqe6GMFEG4AMf+ACXXnopV1xxBV/60pc4/fTTedKTnsSdd94ZW7UtR1U7FpcNtvng+KYNhBfNoJXGP22dZ3HZUNXxDYBzlrI2wb9P0NE6cBBOMITsHy2hrE30D7wQUNWW2gnauUINAhUNSgVjUDtBVdvou1PvoV8GfQst8QhqE04utQlT2AotqZ2gX8Y3sBAWeIQcblpWM9jUIOTQQGw2JsoAvO1tb+P5z38+F198MaeeeipXXnklnU6HP//zP4+t2pZD4DGE9Dnb7Pz2d3UeDK4JBcZlkOUDHHTHN7jG3qFaazHOokUwnr6pqHaNbgP9tHAYZ4dB7VhYa6lc2FEHd1Uzc4GwKbBNfEDiqMZC32D4D0nWBfnNxsTEAKqq4uabb+byyy8f3pNS8oQnPIHPfe5z+/2Zsiwpy3L4emFhYcP13Cp4PBiPkWF5l/jhdC1HiAc453DGgPNBPjLOhYCvEoJ8PxNWBr2AKiOa1L+4W2qlJEpKHPKgdQsOGcZwRs6sCYHTEEGVkn3Gag5SQq0LL2KfWJQK7bRVU9Ao8PuZER2eeynY6wC2KZiYE8Bdd92FtZajjjpqj/tHHXUUd9xxx35/ZufOnczNzQ2/jjvuuPtC1S2BkoqsJSlrjxp80Adr/KrpWmXtyVoSJeN/eoTwSBlcPPvzQQ982FqGIishYvuoBVorrLV0q5ANtPrE4pyjW4WdtNYqusGydjAXeqVL7OoTCzBcbI3z0XfUoS1IOM1KmhNK7SiNo6rDCUbSPJeMhctqvZkYA7AWLr/8cubn54dft912W2yVNg1SCjpKI4WnNA58k98tBVoJ8OGDJIUPcmOQ8icEKJpRlo0+rlmwBr1/tAwFYor4lcC6CVDnmabVZPiYJhtokG3TyiR5psmkQOvYJxbQKhioutHR2LDYGxsyhGrjQrKAir+j1lpRqBD0dwTjlGchaJ1nIS7gICQNKIXW8Tcx683EuIAOP/xwlFL86Ec/2uP+j370I44++uj9/kxRFBRFcV+ot+UQQlDkim3TBVYITGUorcUY0Bqk93RaClUUFPl4jISUUpHnjl7l6BQHHrDSLT3tXCEjn1q89ygJmfcgFa5xrwzqAPAglQRng5si8hbV++Bes85hkUO34MBn5V0YE6pwKCnHYkedZRJVhdhEroJbefBecM5RWY8Snixyiu1GMTH/qjzPecQjHsEnP/nJ4T3nHJ/85Cd51KMeFVGzrYmUklamaeWajtZ0csl0Jum0mmsu6ejwvJXp6AVKQWdBO9OhEKxxqYimICl0AHX0KouW0M7in1qUUmRS4YUiVzIUKcmQUhmu4b4XQW7vLKEY+motMV7SzhRayWY6XFhotAr3jZdoLaPrO2C6nTOdB12q2tKrLFUd/FPTuWK6ncdUb0OZmBMAwKWXXsqFF17IGWecwVlnncXb3/52lpeXufjii2OrtuUQQlA0R+IQWFUgJdJApgEnUFqiJRR6PE4AAEoJZtoZZe0wNnzQh83VhKedKYpMovYTJL6v8R6yXJE37rRMerRWwxOLtZay2blmudrjJBNJY3IlMcoeVN9CBTnGIDFgMLVOCEFL+v1ULsumj1H898NGMFEG4JnPfCY//vGPed3rXscdd9zBwx72MD760Y/uExhObDxSCvImoV5ZT+UlzgukCrs9qSS5lmglyNV4NAALg+zD6aVTSGrTTDZrWkEIAZnWePbNYImjb8j2mW7nw141tXGrKmsF07kizxXOxc+qCb9fQaeVNy0hLHVTDaxkcA9NFwopdXMyiKvwIKNKqybLSgRjpXT4vQ8ymQZtz+Mb2PVH+NiOw/uQhYUF5ubmmJ+fZ3Z2NrY6E433nto4lvo1zgvwFiHkcHHy3oFQSOGZbmVkOm7Z/2oG7X8PlPYnBWPTrGxQaBd2pvtvXrc6wyZu76LgS6/toBeQ3W/vIu89WbNBiLkx8E3rB7kqYylc921f7ZxHq/ibgv3xk6xrE3UCSIwPg92elBLdLKIgkMOh8AIhZNNtcbw+OMMdn181JGZV6uo4nFZg5cRinA87/mbV39t17nzIXor9Ox60BxdCNvOiQxKlUoO6hbDAZlo274mo6gIrhl+seo/urZcfg5qFjSIZgMSaGHTWbGUytFHwAgiZNXLV7imXYiw6a+6NlAfOAhonpBTI5iSwty/aN5XW4zS1Sojg6pFNNtCe8wAEalWWTWz2NrAHYlwM7EaQDEBizXgPmVYo5xsjwNAADD7soWBpPL2MB9rxjRsrJ5amqIqVSmAtx/TE0pwEYF8DG4rtxmNBnTQDu94kA5BYE6vn68qDfNhhPOfrThqTfGIZqOhX3R+nBXVSDOxGkAxAYk0MMlRWXh94Ny0OcD8xGunEsnFMioFdb5IBSKyZ1QG0A7GZA2iJAzOJC+qkGNj1JBmAxJpIAbTEvbEVF9RJI36yc2JikU3jtIN11hw3f28ikVghnQASPxGT6O9NJBKBZAASPzGT6O9NJBLJACTWieTvTSQmjxQDSCQSiS1KMgCJRCKxRUkGIJFIJLYoyQAkEonEFiUZgEQikdiiJAOQSCQSW5RkABKJRGKLkgxAIpFIbFGSAUgkEoktSjIAiUQisUVJBiCRSCS2KMkAJBKJxBYlGYBEIpHYoiQDkEgkEluUZAASiURii5IMQCKRSGxRkgFIJBKJLUoyAIlEIrFFSQYgkUgktijJACQSicQWJRmARCKR2KIkA5BIJBJblIkxAH/wB3/Aox/9aDqdDtu2bYutTiKRSEw8E2MAqqri137t13jhC18YW5VEIpHYFOjYChwqb3jDGwC4+uqr4yqSSCQSm4SJMQBroSxLyrIcvp6fnwdgYWEhlkqJRCKxrgzWM+/9yD+7qQ3Azp07hyeH1Rx33HERtEkkEomN4+6772Zubm6kn4lqAF796lfzlre85aAy/+///T9OOeWUNf35l19+OZdeeunw9e7duznhhBO49dZbR/5FxWBhYYHjjjuO2267jdnZ2djq3CuTpi9Mns5J341l0vSF4Nk4/vjjOeyww0b+2agG4BWveAUXXXTRQWVOOumkNf/5RVFQFMU+9+fm5ibmfy7A7Oxs0neDmTSdk74by6TpCyDl6Dk9UQ3AEUccwRFHHBFThUQikdiyTEwM4NZbb+Wee+7h1ltvxVrLl7/8ZQAe+MAHMj09HVe5RCKRmEAmxgC87nWv433ve9/w9cMf/nAArr/+eh772Mce0p9RFAVXXHHFft1C40jSd+OZNJ2TvhvLpOkLP5nOwq8ldyiRSCQSE8/EVAInEolEYn1JBiCRSCS2KMkAJBKJxBYlGYBEIpHYomxZAzAJ7aXf8Y53cOKJJ9JqtXjkIx/JF77whdgq7ZcbbriB8847j2OPPRYhBH/3d38XW6WDsnPnTs4880xmZmY48sgj+eVf/mW++c1vxlbroLzzne/ktNNOGxYoPepRj+K6666LrdYh8+Y3vxkhBC972ctiq7JfXv/61yOE2ONrrR0I7it+8IMf8OxnP5sdO3bQbrd56EMfyhe/+MWR/owtawDGvb30Bz7wAS699FKuuOIKvvSlL3H66afzpCc9iTvvvDO2avuwvLzM6aefzjve8Y7YqhwSn/70p7nkkkv4/Oc/z8c//nHquuYXf/EXWV5ejq3aAbn//e/Pm9/8Zm6++Wa++MUv8rjHPY5f+qVf4t///d9jq3av3HTTTbzrXe/itNNOi63KQfnpn/5pbr/99uHXZz/72dgqHZBdu3Zx9tlnk2UZ1113Hf/xH//BH/7hH7J9+/bR/iC/xXnve9/r5+bmYquxD2eddZa/5JJLhq+ttf7YY4/1O3fujKjVvQP4a6+9NrYaI3HnnXd6wH/605+OrcpIbN++3b/nPe+JrcZBWVxc9A960IP8xz/+cf+YxzzGv/SlL42t0n654oor/Omnnx5bjUPmVa96lf+5n/u5n/jP2bIngHGmqipuvvlmnvCEJwzvSSl5whOewOc+97mImm1OBm3C19JMKwbWWq655hqWl5d51KMeFVudg3LJJZfw1Kc+dY/38rjy7W9/m2OPPZaTTjqJCy64gFtvvTW2SgfkH/7hHzjjjDP4tV/7NY488kge/vCH8+53v3vkPycZgDHkrrvuwlrLUUcdtcf9o446ijvuuCOSVpsT5xwve9nLOPvss3nIQx4SW52D8rWvfY3p6WmKouAFL3gB1157LaeeempstQ7INddcw5e+9CV27twZW5V75ZGPfCRXX301H/3oR3nnO9/Jd7/7XX7+53+excXF2Krtl//8z//kne98Jw960IP42Mc+xgtf+EJe8pKX7NEt4VCYmFYQh8JGt5dObD4uueQSvv71r4+1v3fAgx/8YL785S8zPz/Phz70IS688EI+/elPj6URuO2223jpS1/Kxz/+cVqtVmx17pVzzz13+P1pp53GIx/5SE444QQ++MEP8rznPS+iZvvHOccZZ5zBm970JiC0xvn617/OlVdeyYUXXnjIf86mMgAb3V76vuLwww9HKcWPfvSjPe7/6Ec/4uijj46k1ebjRS96ER/5yEe44YYbuP/97x9bnXslz3Me+MAHAvCIRzyCm266iT/6oz/iXe96V2TN9uXmm2/mzjvv5Gd+5meG96y13HDDDfzpn/4pZVmilIqo4cHZtm0bJ598MrfccktsVfbLMcccs4/h/6mf+ik+/OEPj/TnbCoDsFnaS+d5ziMe8Qg++clP8su//MtAsPif/OQnedGLXhRXuU2A954Xv/jFXHvttXzqU5/iAQ94QGyV1oRzbo+Rp+PE4x//eL72ta/tce/iiy/mlFNO4VWvetVYL/4AS0tLfOc73+E3fuM3YquyX84+++x9Upe/9a1vccIJJ4z052wqAzAK495e+tJLL+XCCy/kjDPO4KyzzuLtb387y8vLXHzxxbFV24elpaU9dkrf/e53+fKXv8xhhx3G8ccfH1Gz/XPJJZfwN3/zN/z93/89MzMzw7jK3Nwc7XY7snb75/LLL+fcc8/l+OOPZ3Fxkb/5m7/hU5/6FB/72Mdiq7ZfZmZm9ompTE1NsWPHjrGMtVx22WWcd955nHDCCfzwhz/kiiuuQCnF+eefH1u1/fLyl7+cRz/60bzpTW/iGc94Bl/4whe46qqruOqqq0b7g37yhKTJ5MILL/TAPl/XX399bNWG/Mmf/Ik//vjjfZ7n/qyzzvKf//znY6u0X66//vr9/i4vvPDC2Krtl/3pCvj3vve9sVU7IM997nP9CSec4PM890cccYR//OMf7//pn/4ptlojMc5poM985jP9Mccc4/M89/e73/38M5/5TH/LLbfEVuug/J//83/8Qx7yEF8UhT/llFP8VVddNfKfkdpBJxKJxBYlpYEmEonEFiUZgEQikdiiJAOQSCQSW5RkABKJRGKLkgxAIpFIbFGSAUgkEoktSjIAiUQisUVJBiCRSCS2KMkAJBJjzCSM2ExMLskAJCaevWe57v31+te/PraK98rrX/96Hvawh+1z//bbb9+jVXEisZ5s2WZwic3D7bffPvz+Ax/4AK973ev26JS4urmf9x5rLVrfN2/9qqrI83zNP5/afyc2knQCSEw8Rx999PBrbm4OIcTw9Te+8Q1mZma47rrreMQjHkFRFHz2s5/loosuGrbaHvCyl72Mxz72scPXzjl27tzJAx7wANrtNqeffjof+tCHDqrLiSeeyBvf+Eae85znMDs7y3/7b/8NgFe96lWcfPLJdDodTjrpJF772tdS1zUAV199NW94wxv4yle+Mjy1XH311cCeLqDvfe97CCH427/9W8455xw6nQ6nn376PmNC3/3ud3PcccfR6XR4+tOfztve9ja2bdu25t9vYvOSTgCJLcGrX/1q3vrWt3LSSSexffv2Q/qZnTt38ld/9VdceeWVPOhBD+KGG27g2c9+NkcccQSPecxjDvhzb33rW3nd617HFVdcMbw3MzPD1VdfzbHHHsvXvvY1nv/85zMzM8MrX/lKnvnMZ/L1r3+dj370o3ziE58AQmvqA/Ga17yGt771rTzoQQ/iNa95Deeffz633HILWmv+5V/+hRe84AW85S1v4WlPexqf+MQneO1rX3uIv6XEViMZgMSW4Pd+7/d44hOfeMjyZVnypje9iU984hPDwesnnXQSn/3sZ3nXu951UAPwuMc9jle84hV73Pvd3/3d4fcnnngil112Gddccw2vfOUrabfbTE9Po7U+JJfPZZddxlOf+lQA3vCGN/DTP/3T3HLLLZxyyin8yZ/8Ceeeey6XXXYZACeffDI33ngjH/nIRw75357YOiQDkNgSnHHGGSPJ33LLLXS73X2MRlVVPPzhDx/57/rABz7AH//xH/Od73yHpaUljDHMzs6OpNOA0047bfj9McccA8Cdd97JKaecwje/+U2e/vSn7yF/1llnJQOQ2C/JACS2BFNTU3u8llKy9yiMgU8ewpQzgH/8x3/kfve73x5yRVGM9Hd97nOf44ILLuANb3gDT3rSk5ibm+Oaa67hD//wD0f+dwBkWTb8XggBhHhFIjEqyQAktiRHHHEEX//61/e49+Uvf3m4uJ566qkURcGtt956UHfPoXDjjTdywgkn8JrXvGZ47/vf//4eMnmeY639if4egAc/+MHcdNNNe9zb+3UiMSAZgMSW5HGPexz/43/8D/7iL/6CRz3qUfzVX/0VX//614funZmZGS677DJe/vKX45zj537u55ifn+df/uVfmJ2d5cILLzzkv+tBD3oQt956K9dccw1nnnkm//iP/8i11167h8yJJ544nKV8//vfn5mZmXs9aeyPF7/4xfzCL/wCb3vb2zjvvPP453/+Z6677rrhSSGRWE1KA01sSZ70pCfx2te+lle+8pWceeaZLC4u8pznPGcPmTe+8Y289rWvZefOnfzUT/0UT37yk/nHf/xHHvCAB4z0dz3taU/j5S9/OS960Yt42MMexo033rhPZs6v/uqv8uQnP5lzzjmHI444gve///1r+nedffbZXHnllbztbW/j9NNP56Mf/Sgvf/nLabVaa/rzEpubNBM4kdjkPP/5z+cb3/gGn/nMZ2KrkhgzkgsokdhkvPWtb+WJT3wiU1NTXHfddbzvfe/jz/7sz2KrlRhD0gkgkdhkPOMZz+BTn/oUi4uLnHTSSbz4xS/mBS94QWy1EmNIMgCJRCKxRUlB4EQikdiiJAOQSCQSW5RkABKJRGKLkgxAIpFIbFGSAUgkEoktSjIAiUQisUVJBiCRSCS2KMkAJBKJxBbl/wdyjf/Krm9VLQAAAABJRU5ErkJggg==",
+      "text/plain": [
+       "<Figure size 400x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "from sklearn.metrics import mean_squared_error\n",
     "from sklearn.metrics import mean_absolute_error\n",
@@ -467,9 +1450,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 19,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[(944, 64), (1683, 64)]"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "# weights and shape\n",
     "weights = model.get_weights()\n",
@@ -478,7 +1472,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 20,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -488,9 +1482,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 24,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Title for item_id=181: Return of the Jedi (1983)\n"
+     ]
+    }
+   ],
    "source": [
     "item_id = 181\n",
     "print(f\"Title for item_id={item_id}: {indexed_items['title'][item_id]}\")"
@@ -498,9 +1500,29 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 25,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Embedding vector for item_id=181\n",
+      "[-0.24699579  0.17448889 -0.39920208  0.41591346 -0.16879232 -0.06813098\n",
+      "  0.297689    0.01532099  0.2950429   0.15335052  0.4156495   0.12227755\n",
+      " -0.5200263  -0.30489334 -0.02281587 -0.33929563  0.3696327  -0.503899\n",
+      " -0.4143098  -0.40008634 -0.11241761  0.39848435 -0.08853704 -0.15797502\n",
+      " -0.32311738 -0.3521137  -0.11892717  0.2804213  -0.43349308 -0.30939806\n",
+      "  0.43746558  0.2934358  -0.176093   -0.4300282  -0.1123656  -0.16426873\n",
+      "  0.50522727 -0.46124858 -0.41893128  0.26342082  0.34562206  0.41487974\n",
+      "  0.05577465 -0.21713503 -0.42845035 -0.12033463  0.31292877 -0.32873467\n",
+      "  0.18581825  0.4654977  -0.0921814  -0.05791562  0.36046028 -0.18180555\n",
+      " -0.43847007  0.15883568 -0.46489823 -0.33253032 -0.35733426  0.30606604\n",
+      "  0.19424848  0.1883304  -0.34077162  0.40843448]\n",
+      "shape: (64,)\n"
+     ]
+    }
+   ],
    "source": [
     "print(f\"Embedding vector for item_id={item_id}\")\n",
     "print(item_embeddings[item_id])\n",
@@ -527,7 +1549,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 26,
    "metadata": {
     "collapsed": false
    },
@@ -544,9 +1566,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 27,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Star Wars (1977)\n",
+      "Return of the Jedi (1983)\n",
+      "Cosine similarity: 0.911\n"
+     ]
+    }
+   ],
    "source": [
     "def print_similarity(item_a, item_b, item_embeddings, titles):\n",
     "    print(titles[item_a])\n",
@@ -569,27 +1601,57 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 28,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Return of the Jedi (1983)\n",
+      "Scream (1996)\n",
+      "Cosine similarity: 0.782\n"
+     ]
+    }
+   ],
    "source": [
     "print_similarity(181, 288, item_embeddings, indexed_items[\"title\"])"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 29,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Return of the Jedi (1983)\n",
+      "Toy Story (1995)\n",
+      "Cosine similarity: 0.818\n"
+     ]
+    }
+   ],
    "source": [
     "print_similarity(181, 1, item_embeddings, indexed_items[\"title\"])"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 30,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Return of the Jedi (1983)\n",
+      "Return of the Jedi (1983)\n",
+      "Cosine similarity: 1.0\n"
+     ]
+    }
+   ],
    "source": [
     "print_similarity(181, 181, item_embeddings, indexed_items[\"title\"])"
    ]
@@ -607,17 +1669,50 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 32,
    "metadata": {
     "collapsed": false
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Jedi movies found:\n",
+      "                             title\n",
+      "item_id                           \n",
+      "181      Return of the Jedi (1983)\n",
+      "\n",
+      "First Jedi movie ID: 181\n",
+      "\n",
+      "Similarity comparisons:\n",
+      "Return of the Jedi (1983)\n",
+      "Return of the Jedi (1983)\n",
+      "Cosine similarity: 1.0\n",
+      "Return of the Jedi (1983)\n",
+      "Star Wars (1977)\n",
+      "Cosine similarity: 0.911\n",
+      "Return of the Jedi (1983)\n",
+      "Toy Story (1995)\n",
+      "Cosine similarity: 0.818\n"
+     ]
+    }
+   ],
    "source": [
-    "# Code to help you search for a movie title\n",
     "partial_title = \"Jedi\"\n",
-    "indexed_items[indexed_items['title'].str.contains(partial_title)]\n",
+    "jedi_movies = indexed_items[indexed_items['title'].str.contains(partial_title)]\n",
+    "print(\"Jedi movies found:\")\n",
+    "print(jedi_movies[['title']])\n",
     "\n",
-    "raise NotImplementedError(\"Please implement the next steps yourself\")"
+    "# Get the first Jedi movie ID\n",
+    "jedi_id = jedi_movies.index[0]\n",
+    "print(f\"\\nFirst Jedi movie ID: {jedi_id}\")\n",
+    "\n",
+    "# Compare with different movies\n",
+    "print(\"\\nSimilarity comparisons:\")\n",
+    "print_similarity(jedi_id, jedi_id, item_embeddings, indexed_items[\"title\"])  # Same movie\n",
+    "print_similarity(jedi_id, 50, item_embeddings, indexed_items[\"title\"])        # Star Wars\n",
+    "print_similarity(jedi_id, 1, item_embeddings, indexed_items[\"title\"])         # Toy Story"
    ]
   },
   {
@@ -631,9 +1726,29 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 33,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[(np.int64(50), 'Star Wars (1977)', np.float32(1.0)),\n",
+       " (np.int64(172), 'Empire Strikes Back, The (1980)', np.float32(0.9205836)),\n",
+       " (np.int64(181), 'Return of the Jedi (1983)', np.float32(0.91098756)),\n",
+       " (np.int64(174), 'Raiders of the Lost Ark (1981)', np.float32(0.86638635)),\n",
+       " (np.int64(423), 'E.T. the Extra-Terrestrial (1982)', np.float32(0.8596404)),\n",
+       " (np.int64(98), 'Silence of the Lambs, The (1991)', np.float32(0.8586037)),\n",
+       " (np.int64(205), 'Patton (1970)', np.float32(0.8516545)),\n",
+       " (np.int64(187), 'Godfather: Part II, The (1974)', np.float32(0.8497994)),\n",
+       " (np.int64(144), 'Die Hard (1988)', np.float32(0.84906673)),\n",
+       " (np.int64(520), 'Great Escape, The (1963)', np.float32(0.8485322))]"
+      ]
+     },
+     "execution_count": 33,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "def most_similar(item_id, item_embeddings, titles,\n",
     "                 top_n=30):\n",
@@ -654,9 +1769,35 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 34,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[(np.int64(227),\n",
+       "  'Star Trek VI: The Undiscovered Country (1991)',\n",
+       "  np.float32(1.0000001)),\n",
+       " (np.int64(230),\n",
+       "  'Star Trek IV: The Voyage Home (1986)',\n",
+       "  np.float32(0.86224747)),\n",
+       " (np.int64(101), 'Heavy Metal (1981)', np.float32(0.8619977)),\n",
+       " (np.int64(1552), 'Hunted, The (1995)', np.float32(0.85914284)),\n",
+       " (np.int64(228),\n",
+       "  'Star Trek: The Wrath of Khan (1982)',\n",
+       "  np.float32(0.85553956)),\n",
+       " (np.int64(399), 'Three Musketeers, The (1993)', np.float32(0.84906065)),\n",
+       " (np.int64(164), 'Abyss, The (1989)', np.float32(0.8416189)),\n",
+       " (np.int64(195), 'Terminator, The (1984)', np.float32(0.8414033)),\n",
+       " (np.int64(778), 'Don Juan DeMarco (1995)', np.float32(0.840008)),\n",
+       " (np.int64(222), 'Star Trek: First Contact (1996)', np.float32(0.83815885))]"
+      ]
+     },
+     "execution_count": 34,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "# Find the most similar films to \"Star Trek VI: The Undiscovered Country\"\n",
     "most_similar(227, item_embeddings, indexed_items[\"title\"], top_n=10)"
@@ -680,7 +1821,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 35,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -691,9 +1832,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 36,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 1000x1000 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "import matplotlib.pyplot as plt\n",
     "\n",
@@ -705,11 +1857,9316 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 37,
    "metadata": {
     "tags": []
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.plotly.v1+json": {
+       "config": {
+        "plotlyServerURL": "https://plot.ly"
+       },
+       "data": [
+        {
+         "customdata": [
+          [
+           1,
+           "Toy Story (1995)",
+           452
+          ],
+          [
+           2,
+           "GoldenEye (1995)",
+           131
+          ],
+          [
+           3,
+           "Four Rooms (1995)",
+           90
+          ],
+          [
+           4,
+           "Get Shorty (1995)",
+           209
+          ],
+          [
+           5,
+           "Copycat (1995)",
+           86
+          ],
+          [
+           6,
+           "Shanghai Triad (Yao a yao yao dao waipo qiao) (1995)",
+           26
+          ],
+          [
+           7,
+           "Twelve Monkeys (1995)",
+           392
+          ],
+          [
+           8,
+           "Babe (1995)",
+           219
+          ],
+          [
+           9,
+           "Dead Man Walking (1995)",
+           299
+          ],
+          [
+           10,
+           "Richard III (1995)",
+           89
+          ],
+          [
+           11,
+           "Seven (Se7en) (1995)",
+           236
+          ],
+          [
+           12,
+           "Usual Suspects, The (1995)",
+           267
+          ],
+          [
+           13,
+           "Mighty Aphrodite (1995)",
+           184
+          ],
+          [
+           14,
+           "Postino, Il (1994)",
+           183
+          ],
+          [
+           15,
+           "Mr. Holland's Opus (1995)",
+           293
+          ],
+          [
+           16,
+           "French Twist (Gazon maudit) (1995)",
+           39
+          ],
+          [
+           17,
+           "From Dusk Till Dawn (1996)",
+           92
+          ],
+          [
+           18,
+           "White Balloon, The (1995)",
+           10
+          ],
+          [
+           19,
+           "Antonia's Line (1995)",
+           69
+          ],
+          [
+           20,
+           "Angels and Insects (1995)",
+           72
+          ],
+          [
+           21,
+           "Muppet Treasure Island (1996)",
+           84
+          ],
+          [
+           22,
+           "Braveheart (1995)",
+           297
+          ],
+          [
+           23,
+           "Taxi Driver (1976)",
+           182
+          ],
+          [
+           24,
+           "Rumble in the Bronx (1995)",
+           174
+          ],
+          [
+           25,
+           "Birdcage, The (1996)",
+           293
+          ],
+          [
+           26,
+           "Brothers McMullen, The (1995)",
+           73
+          ],
+          [
+           27,
+           "Bad Boys (1995)",
+           57
+          ],
+          [
+           28,
+           "Apollo 13 (1995)",
+           276
+          ],
+          [
+           29,
+           "Batman Forever (1995)",
+           114
+          ],
+          [
+           30,
+           "Belle de jour (1967)",
+           37
+          ],
+          [
+           31,
+           "Crimson Tide (1995)",
+           154
+          ],
+          [
+           32,
+           "Crumb (1994)",
+           81
+          ],
+          [
+           33,
+           "Desperado (1995)",
+           97
+          ],
+          [
+           34,
+           "Doom Generation, The (1995)",
+           7
+          ],
+          [
+           35,
+           "Free Willy 2: The Adventure Home (1995)",
+           11
+          ],
+          [
+           36,
+           "Mad Love (1995)",
+           13
+          ],
+          [
+           37,
+           "Nadja (1994)",
+           8
+          ],
+          [
+           38,
+           "Net, The (1995)",
+           120
+          ],
+          [
+           39,
+           "Strange Days (1995)",
+           87
+          ],
+          [
+           40,
+           "To Wong Foo, Thanks for Everything! Julie Newmar (1995)",
+           57
+          ],
+          [
+           41,
+           "Billy Madison (1995)",
+           37
+          ],
+          [
+           42,
+           "Clerks (1994)",
+           148
+          ],
+          [
+           43,
+           "Disclosure (1994)",
+           40
+          ],
+          [
+           44,
+           "Dolores Claiborne (1994)",
+           79
+          ],
+          [
+           45,
+           "Eat Drink Man Woman (1994)",
+           80
+          ],
+          [
+           46,
+           "Exotica (1994)",
+           27
+          ],
+          [
+           47,
+           "Ed Wood (1994)",
+           133
+          ],
+          [
+           48,
+           "Hoop Dreams (1994)",
+           117
+          ],
+          [
+           49,
+           "I.Q. (1994)",
+           81
+          ],
+          [
+           50,
+           "Star Wars (1977)",
+           583
+          ],
+          [
+           51,
+           "Legends of the Fall (1994)",
+           81
+          ],
+          [
+           52,
+           "Madness of King George, The (1994)",
+           91
+          ],
+          [
+           53,
+           "Natural Born Killers (1994)",
+           128
+          ],
+          [
+           54,
+           "Outbreak (1995)",
+           104
+          ],
+          [
+           55,
+           "Professional, The (1994)",
+           149
+          ],
+          [
+           56,
+           "Pulp Fiction (1994)",
+           394
+          ],
+          [
+           57,
+           "Priest (1994)",
+           40
+          ],
+          [
+           58,
+           "Quiz Show (1994)",
+           175
+          ],
+          [
+           59,
+           "Three Colors: Red (1994)",
+           83
+          ],
+          [
+           60,
+           "Three Colors: Blue (1993)",
+           64
+          ],
+          [
+           61,
+           "Three Colors: White (1994)",
+           59
+          ],
+          [
+           62,
+           "Stargate (1994)",
+           127
+          ],
+          [
+           63,
+           "Santa Clause, The (1994)",
+           82
+          ],
+          [
+           64,
+           "Shawshank Redemption, The (1994)",
+           283
+          ],
+          [
+           65,
+           "What's Eating Gilbert Grape (1993)",
+           115
+          ],
+          [
+           66,
+           "While You Were Sleeping (1995)",
+           162
+          ],
+          [
+           67,
+           "Ace Ventura: Pet Detective (1994)",
+           103
+          ],
+          [
+           68,
+           "Crow, The (1994)",
+           134
+          ],
+          [
+           69,
+           "Forrest Gump (1994)",
+           321
+          ],
+          [
+           70,
+           "Four Weddings and a Funeral (1994)",
+           251
+          ],
+          [
+           71,
+           "Lion King, The (1994)",
+           220
+          ],
+          [
+           72,
+           "Mask, The (1994)",
+           129
+          ],
+          [
+           73,
+           "Maverick (1994)",
+           128
+          ],
+          [
+           74,
+           "Faster Pussycat! Kill! Kill! (1965)",
+           7
+          ],
+          [
+           75,
+           "Brother Minister: The Assassination of Malcolm X (1994)",
+           5
+          ],
+          [
+           76,
+           "Carlito's Way (1993)",
+           54
+          ],
+          [
+           77,
+           "Firm, The (1993)",
+           151
+          ],
+          [
+           78,
+           "Free Willy (1993)",
+           33
+          ],
+          [
+           79,
+           "Fugitive, The (1993)",
+           336
+          ],
+          [
+           80,
+           "Hot Shots! Part Deux (1993)",
+           68
+          ],
+          [
+           81,
+           "Hudsucker Proxy, The (1994)",
+           110
+          ],
+          [
+           82,
+           "Jurassic Park (1993)",
+           261
+          ],
+          [
+           83,
+           "Much Ado About Nothing (1993)",
+           176
+          ],
+          [
+           84,
+           "Robert A. Heinlein's The Puppet Masters (1994)",
+           18
+          ],
+          [
+           85,
+           "Ref, The (1994)",
+           58
+          ],
+          [
+           86,
+           "Remains of the Day, The (1993)",
+           150
+          ],
+          [
+           87,
+           "Searching for Bobby Fischer (1993)",
+           138
+          ],
+          [
+           88,
+           "Sleepless in Seattle (1993)",
+           213
+          ],
+          [
+           89,
+           "Blade Runner (1982)",
+           275
+          ],
+          [
+           90,
+           "So I Married an Axe Murderer (1993)",
+           95
+          ],
+          [
+           91,
+           "Nightmare Before Christmas, The (1993)",
+           143
+          ],
+          [
+           92,
+           "True Romance (1993)",
+           104
+          ],
+          [
+           93,
+           "Welcome to the Dollhouse (1995)",
+           112
+          ],
+          [
+           94,
+           "Home Alone (1990)",
+           137
+          ],
+          [
+           95,
+           "Aladdin (1992)",
+           219
+          ],
+          [
+           96,
+           "Terminator 2: Judgment Day (1991)",
+           295
+          ],
+          [
+           97,
+           "Dances with Wolves (1990)",
+           256
+          ],
+          [
+           98,
+           "Silence of the Lambs, The (1991)",
+           390
+          ],
+          [
+           99,
+           "Snow White and the Seven Dwarfs (1937)",
+           172
+          ],
+          [
+           100,
+           "Fargo (1996)",
+           508
+          ],
+          [
+           101,
+           "Heavy Metal (1981)",
+           73
+          ],
+          [
+           102,
+           "Aristocats, The (1970)",
+           54
+          ],
+          [
+           103,
+           "All Dogs Go to Heaven 2 (1996)",
+           15
+          ],
+          [
+           104,
+           "Theodore Rex (1995)",
+           5
+          ],
+          [
+           105,
+           "Sgt. Bilko (1996)",
+           74
+          ],
+          [
+           106,
+           "Diabolique (1996)",
+           71
+          ],
+          [
+           107,
+           "Moll Flanders (1996)",
+           42
+          ],
+          [
+           108,
+           "Kids in the Hall: Brain Candy (1996)",
+           65
+          ],
+          [
+           109,
+           "Mystery Science Theater 3000: The Movie (1996)",
+           130
+          ],
+          [
+           110,
+           "Operation Dumbo Drop (1995)",
+           31
+          ],
+          [
+           111,
+           "Truth About Cats & Dogs, The (1996)",
+           272
+          ],
+          [
+           112,
+           "Flipper (1996)",
+           20
+          ],
+          [
+           113,
+           "Horseman on the Roof, The (Hussard sur le toit, Le) (1995)",
+           9
+          ],
+          [
+           114,
+           "Wallace & Gromit: The Best of Aardman Animation (1996)",
+           67
+          ],
+          [
+           115,
+           "Haunted World of Edward D. Wood Jr., The (1995)",
+           15
+          ],
+          [
+           116,
+           "Cold Comfort Farm (1995)",
+           125
+          ],
+          [
+           117,
+           "Rock, The (1996)",
+           378
+          ],
+          [
+           118,
+           "Twister (1996)",
+           293
+          ],
+          [
+           119,
+           "Maya Lin: A Strong Clear Vision (1994)",
+           4
+          ],
+          [
+           120,
+           "Striptease (1996)",
+           67
+          ],
+          [
+           121,
+           "Independence Day (ID4) (1996)",
+           429
+          ],
+          [
+           122,
+           "Cable Guy, The (1996)",
+           106
+          ],
+          [
+           123,
+           "Frighteners, The (1996)",
+           115
+          ],
+          [
+           124,
+           "Lone Star (1996)",
+           187
+          ],
+          [
+           125,
+           "Phenomenon (1996)",
+           244
+          ],
+          [
+           126,
+           "Spitfire Grill, The (1996)",
+           97
+          ],
+          [
+           127,
+           "Godfather, The (1972)",
+           413
+          ],
+          [
+           128,
+           "Supercop (1992)",
+           65
+          ],
+          [
+           129,
+           "Bound (1996)",
+           129
+          ],
+          [
+           130,
+           "Kansas City (1996)",
+           23
+          ],
+          [
+           131,
+           "Breakfast at Tiffany's (1961)",
+           95
+          ],
+          [
+           132,
+           "Wizard of Oz, The (1939)",
+           246
+          ],
+          [
+           133,
+           "Gone with the Wind (1939)",
+           171
+          ],
+          [
+           134,
+           "Citizen Kane (1941)",
+           198
+          ],
+          [
+           135,
+           "2001: A Space Odyssey (1968)",
+           259
+          ],
+          [
+           136,
+           "Mr. Smith Goes to Washington (1939)",
+           105
+          ],
+          [
+           137,
+           "Big Night (1996)",
+           171
+          ],
+          [
+           138,
+           "D3: The Mighty Ducks (1996)",
+           19
+          ],
+          [
+           139,
+           "Love Bug, The (1969)",
+           50
+          ],
+          [
+           140,
+           "Homeward Bound: The Incredible Journey (1993)",
+           61
+          ],
+          [
+           141,
+           "20,000 Leagues Under the Sea (1954)",
+           72
+          ],
+          [
+           142,
+           "Bedknobs and Broomsticks (1971)",
+           57
+          ],
+          [
+           143,
+           "Sound of Music, The (1965)",
+           222
+          ],
+          [
+           144,
+           "Die Hard (1988)",
+           243
+          ],
+          [
+           145,
+           "Lawnmower Man, The (1992)",
+           65
+          ],
+          [
+           146,
+           "Unhook the Stars (1996)",
+           10
+          ],
+          [
+           147,
+           "Long Kiss Goodnight, The (1996)",
+           185
+          ],
+          [
+           148,
+           "Ghost and the Darkness, The (1996)",
+           128
+          ],
+          [
+           149,
+           "Jude (1996)",
+           23
+          ],
+          [
+           150,
+           "Swingers (1996)",
+           157
+          ],
+          [
+           151,
+           "Willy Wonka and the Chocolate Factory (1971)",
+           326
+          ],
+          [
+           152,
+           "Sleeper (1973)",
+           82
+          ],
+          [
+           153,
+           "Fish Called Wanda, A (1988)",
+           247
+          ],
+          [
+           154,
+           "Monty Python's Life of Brian (1979)",
+           174
+          ],
+          [
+           155,
+           "Dirty Dancing (1987)",
+           98
+          ],
+          [
+           156,
+           "Reservoir Dogs (1992)",
+           148
+          ],
+          [
+           157,
+           "Platoon (1986)",
+           127
+          ],
+          [
+           158,
+           "Weekend at Bernie's (1989)",
+           60
+          ],
+          [
+           159,
+           "Basic Instinct (1992)",
+           101
+          ],
+          [
+           160,
+           "Glengarry Glen Ross (1992)",
+           69
+          ],
+          [
+           161,
+           "Top Gun (1986)",
+           220
+          ],
+          [
+           162,
+           "On Golden Pond (1981)",
+           106
+          ],
+          [
+           163,
+           "Return of the Pink Panther, The (1974)",
+           92
+          ],
+          [
+           164,
+           "Abyss, The (1989)",
+           151
+          ],
+          [
+           165,
+           "Jean de Florette (1986)",
+           64
+          ],
+          [
+           166,
+           "Manon of the Spring (Manon des sources) (1986)",
+           58
+          ],
+          [
+           167,
+           "Private Benjamin (1980)",
+           67
+          ],
+          [
+           168,
+           "Monty Python and the Holy Grail (1974)",
+           316
+          ],
+          [
+           169,
+           "Wrong Trousers, The (1993)",
+           118
+          ],
+          [
+           170,
+           "Cinema Paradiso (1988)",
+           121
+          ],
+          [
+           171,
+           "Delicatessen (1991)",
+           65
+          ],
+          [
+           172,
+           "Empire Strikes Back, The (1980)",
+           367
+          ],
+          [
+           173,
+           "Princess Bride, The (1987)",
+           324
+          ],
+          [
+           174,
+           "Raiders of the Lost Ark (1981)",
+           420
+          ],
+          [
+           175,
+           "Brazil (1985)",
+           208
+          ],
+          [
+           176,
+           "Aliens (1986)",
+           284
+          ],
+          [
+           177,
+           "Good, The Bad and The Ugly, The (1966)",
+           137
+          ],
+          [
+           178,
+           "12 Angry Men (1957)",
+           125
+          ],
+          [
+           179,
+           "Clockwork Orange, A (1971)",
+           221
+          ],
+          [
+           180,
+           "Apocalypse Now (1979)",
+           221
+          ],
+          [
+           181,
+           "Return of the Jedi (1983)",
+           507
+          ],
+          [
+           182,
+           "GoodFellas (1990)",
+           226
+          ],
+          [
+           183,
+           "Alien (1979)",
+           291
+          ],
+          [
+           184,
+           "Army of Darkness (1993)",
+           116
+          ],
+          [
+           185,
+           "Psycho (1960)",
+           239
+          ],
+          [
+           186,
+           "Blues Brothers, The (1980)",
+           251
+          ],
+          [
+           187,
+           "Godfather: Part II, The (1974)",
+           209
+          ],
+          [
+           188,
+           "Full Metal Jacket (1987)",
+           170
+          ],
+          [
+           189,
+           "Grand Day Out, A (1992)",
+           66
+          ],
+          [
+           190,
+           "Henry V (1989)",
+           124
+          ],
+          [
+           191,
+           "Amadeus (1984)",
+           276
+          ],
+          [
+           192,
+           "Raging Bull (1980)",
+           116
+          ],
+          [
+           193,
+           "Right Stuff, The (1983)",
+           157
+          ],
+          [
+           194,
+           "Sting, The (1973)",
+           241
+          ],
+          [
+           195,
+           "Terminator, The (1984)",
+           301
+          ],
+          [
+           196,
+           "Dead Poets Society (1989)",
+           251
+          ],
+          [
+           197,
+           "Graduate, The (1967)",
+           239
+          ],
+          [
+           198,
+           "Nikita (La Femme Nikita) (1990)",
+           127
+          ],
+          [
+           199,
+           "Bridge on the River Kwai, The (1957)",
+           165
+          ],
+          [
+           200,
+           "Shining, The (1980)",
+           206
+          ],
+          [
+           201,
+           "Evil Dead II (1987)",
+           89
+          ],
+          [
+           202,
+           "Groundhog Day (1993)",
+           280
+          ],
+          [
+           203,
+           "Unforgiven (1992)",
+           182
+          ],
+          [
+           204,
+           "Back to the Future (1985)",
+           350
+          ],
+          [
+           205,
+           "Patton (1970)",
+           136
+          ],
+          [
+           206,
+           "Akira (1988)",
+           50
+          ],
+          [
+           207,
+           "Cyrano de Bergerac (1990)",
+           66
+          ],
+          [
+           208,
+           "Young Frankenstein (1974)",
+           200
+          ],
+          [
+           209,
+           "This Is Spinal Tap (1984)",
+           191
+          ],
+          [
+           210,
+           "Indiana Jones and the Last Crusade (1989)",
+           331
+          ],
+          [
+           211,
+           "M*A*S*H (1970)",
+           206
+          ],
+          [
+           212,
+           "Unbearable Lightness of Being, The (1988)",
+           92
+          ],
+          [
+           213,
+           "Room with a View, A (1986)",
+           134
+          ],
+          [
+           214,
+           "Pink Floyd - The Wall (1982)",
+           114
+          ],
+          [
+           215,
+           "Field of Dreams (1989)",
+           212
+          ],
+          [
+           216,
+           "When Harry Met Sally... (1989)",
+           290
+          ],
+          [
+           217,
+           "Bram Stoker's Dracula (1992)",
+           120
+          ],
+          [
+           218,
+           "Cape Fear (1991)",
+           171
+          ],
+          [
+           219,
+           "Nightmare on Elm Street, A (1984)",
+           111
+          ],
+          [
+           220,
+           "Mirror Has Two Faces, The (1996)",
+           66
+          ],
+          [
+           221,
+           "Breaking the Waves (1996)",
+           74
+          ],
+          [
+           222,
+           "Star Trek: First Contact (1996)",
+           365
+          ],
+          [
+           223,
+           "Sling Blade (1996)",
+           136
+          ],
+          [
+           224,
+           "Ridicule (1996)",
+           44
+          ],
+          [
+           225,
+           "101 Dalmatians (1996)",
+           109
+          ],
+          [
+           226,
+           "Die Hard 2 (1990)",
+           166
+          ],
+          [
+           227,
+           "Star Trek VI: The Undiscovered Country (1991)",
+           161
+          ],
+          [
+           228,
+           "Star Trek: The Wrath of Khan (1982)",
+           244
+          ],
+          [
+           229,
+           "Star Trek III: The Search for Spock (1984)",
+           171
+          ],
+          [
+           230,
+           "Star Trek IV: The Voyage Home (1986)",
+           199
+          ],
+          [
+           231,
+           "Batman Returns (1992)",
+           142
+          ],
+          [
+           232,
+           "Young Guns (1988)",
+           101
+          ],
+          [
+           233,
+           "Under Siege (1992)",
+           124
+          ],
+          [
+           234,
+           "Jaws (1975)",
+           280
+          ],
+          [
+           235,
+           "Mars Attacks! (1996)",
+           217
+          ],
+          [
+           236,
+           "Citizen Ruth (1996)",
+           45
+          ],
+          [
+           237,
+           "Jerry Maguire (1996)",
+           384
+          ],
+          [
+           238,
+           "Raising Arizona (1987)",
+           256
+          ],
+          [
+           239,
+           "Sneakers (1992)",
+           150
+          ],
+          [
+           240,
+           "Beavis and Butt-head Do America (1996)",
+           156
+          ],
+          [
+           241,
+           "Last of the Mohicans, The (1992)",
+           128
+          ],
+          [
+           242,
+           "Kolya (1996)",
+           117
+          ],
+          [
+           243,
+           "Jungle2Jungle (1997)",
+           132
+          ],
+          [
+           244,
+           "Smilla's Sense of Snow (1997)",
+           48
+          ],
+          [
+           245,
+           "Devil's Own, The (1997)",
+           240
+          ],
+          [
+           246,
+           "Chasing Amy (1997)",
+           124
+          ],
+          [
+           247,
+           "Turbo: A Power Rangers Movie (1997)",
+           5
+          ],
+          [
+           248,
+           "Grosse Pointe Blank (1997)",
+           160
+          ],
+          [
+           249,
+           "Austin Powers: International Man of Mystery (1997)",
+           130
+          ],
+          [
+           250,
+           "Fifth Element, The (1997)",
+           197
+          ],
+          [
+           251,
+           "Shall We Dance? (1996)",
+           46
+          ],
+          [
+           252,
+           "Lost World: Jurassic Park, The (1997)",
+           158
+          ],
+          [
+           253,
+           "Pillow Book, The (1995)",
+           26
+          ],
+          [
+           254,
+           "Batman & Robin (1997)",
+           62
+          ],
+          [
+           255,
+           "My Best Friend's Wedding (1997)",
+           172
+          ],
+          [
+           256,
+           "When the Cats Away (Chacun cherche son chat) (1996)",
+           16
+          ],
+          [
+           257,
+           "Men in Black (1997)",
+           303
+          ],
+          [
+           258,
+           "Contact (1997)",
+           509
+          ],
+          [
+           259,
+           "George of the Jungle (1997)",
+           162
+          ],
+          [
+           260,
+           "Event Horizon (1997)",
+           127
+          ],
+          [
+           261,
+           "Air Bud (1997)",
+           43
+          ],
+          [
+           262,
+           "In the Company of Men (1997)",
+           66
+          ],
+          [
+           263,
+           "Steel (1997)",
+           19
+          ],
+          [
+           264,
+           "Mimic (1997)",
+           101
+          ],
+          [
+           265,
+           "Hunt for Red October, The (1990)",
+           227
+          ],
+          [
+           266,
+           "Kull the Conqueror (1997)",
+           35
+          ],
+          [
+           267,
+           "unknown",
+           9
+          ],
+          [
+           268,
+           "Chasing Amy (1997)",
+           255
+          ],
+          [
+           269,
+           "Full Monty, The (1997)",
+           315
+          ],
+          [
+           270,
+           "Gattaca (1997)",
+           136
+          ],
+          [
+           271,
+           "Starship Troopers (1997)",
+           211
+          ],
+          [
+           272,
+           "Good Will Hunting (1997)",
+           198
+          ],
+          [
+           273,
+           "Heat (1995)",
+           223
+          ],
+          [
+           274,
+           "Sabrina (1995)",
+           190
+          ],
+          [
+           275,
+           "Sense and Sensibility (1995)",
+           268
+          ],
+          [
+           276,
+           "Leaving Las Vegas (1995)",
+           298
+          ],
+          [
+           277,
+           "Restoration (1995)",
+           71
+          ],
+          [
+           278,
+           "Bed of Roses (1996)",
+           60
+          ],
+          [
+           279,
+           "Once Upon a Time... When We Were Colored (1995)",
+           28
+          ],
+          [
+           280,
+           "Up Close and Personal (1996)",
+           85
+          ],
+          [
+           281,
+           "River Wild, The (1994)",
+           146
+          ],
+          [
+           282,
+           "Time to Kill, A (1996)",
+           232
+          ],
+          [
+           283,
+           "Emma (1996)",
+           177
+          ],
+          [
+           284,
+           "Tin Cup (1996)",
+           193
+          ],
+          [
+           285,
+           "Secrets & Lies (1996)",
+           162
+          ],
+          [
+           286,
+           "English Patient, The (1996)",
+           481
+          ],
+          [
+           287,
+           "Marvin's Room (1996)",
+           78
+          ],
+          [
+           288,
+           "Scream (1996)",
+           478
+          ],
+          [
+           289,
+           "Evita (1996)",
+           259
+          ],
+          [
+           290,
+           "Fierce Creatures (1997)",
+           96
+          ],
+          [
+           291,
+           "Absolute Power (1997)",
+           127
+          ],
+          [
+           292,
+           "Rosewood (1997)",
+           114
+          ],
+          [
+           293,
+           "Donnie Brasco (1997)",
+           147
+          ],
+          [
+           294,
+           "Liar Liar (1997)",
+           485
+          ],
+          [
+           295,
+           "Breakdown (1997)",
+           77
+          ],
+          [
+           296,
+           "Promesse, La (1996)",
+           6
+          ],
+          [
+           297,
+           "Ulee's Gold (1997)",
+           50
+          ],
+          [
+           298,
+           "Face/Off (1997)",
+           194
+          ],
+          [
+           299,
+           "Hoodlum (1997)",
+           73
+          ],
+          [
+           300,
+           "Air Force One (1997)",
+           431
+          ],
+          [
+           301,
+           "In & Out (1997)",
+           230
+          ],
+          [
+           302,
+           "L.A. Confidential (1997)",
+           297
+          ],
+          [
+           303,
+           "Ulee's Gold (1997)",
+           134
+          ],
+          [
+           304,
+           "Fly Away Home (1996)",
+           149
+          ],
+          [
+           305,
+           "Ice Storm, The (1997)",
+           87
+          ],
+          [
+           306,
+           "Mrs. Brown (Her Majesty, Mrs. Brown) (1997)",
+           96
+          ],
+          [
+           307,
+           "Devil's Advocate, The (1997)",
+           188
+          ],
+          [
+           308,
+           "FairyTale: A True Story (1997)",
+           30
+          ],
+          [
+           309,
+           "Deceiver (1997)",
+           28
+          ],
+          [
+           310,
+           "Rainmaker, The (1997)",
+           145
+          ],
+          [
+           311,
+           "Wings of the Dove, The (1997)",
+           75
+          ],
+          [
+           312,
+           "Midnight in the Garden of Good and Evil (1997)",
+           80
+          ],
+          [
+           313,
+           "Titanic (1997)",
+           350
+          ],
+          [
+           314,
+           "3 Ninjas: High Noon At Mega Mountain (1998)",
+           5
+          ],
+          [
+           315,
+           "Apt Pupil (1998)",
+           160
+          ],
+          [
+           316,
+           "As Good As It Gets (1997)",
+           112
+          ],
+          [
+           317,
+           "In the Name of the Father (1993)",
+           102
+          ],
+          [
+           318,
+           "Schindler's List (1993)",
+           298
+          ],
+          [
+           319,
+           "Everyone Says I Love You (1996)",
+           168
+          ],
+          [
+           320,
+           "Paradise Lost: The Child Murders at Robin Hood Hills (1996)",
+           20
+          ],
+          [
+           321,
+           "Mother (1996)",
+           169
+          ],
+          [
+           322,
+           "Murder at 1600 (1997)",
+           218
+          ],
+          [
+           323,
+           "Dante's Peak (1997)",
+           240
+          ],
+          [
+           324,
+           "Lost Highway (1997)",
+           125
+          ],
+          [
+           325,
+           "Crash (1996)",
+           128
+          ],
+          [
+           326,
+           "G.I. Jane (1997)",
+           175
+          ],
+          [
+           327,
+           "Cop Land (1997)",
+           175
+          ],
+          [
+           328,
+           "Conspiracy Theory (1997)",
+           295
+          ],
+          [
+           329,
+           "Desperate Measures (1998)",
+           45
+          ],
+          [
+           330,
+           "187 (1997)",
+           41
+          ],
+          [
+           331,
+           "Edge, The (1997)",
+           113
+          ],
+          [
+           332,
+           "Kiss the Girls (1997)",
+           143
+          ],
+          [
+           333,
+           "Game, The (1997)",
+           251
+          ],
+          [
+           334,
+           "U Turn (1997)",
+           64
+          ],
+          [
+           335,
+           "How to Be a Player (1997)",
+           21
+          ],
+          [
+           336,
+           "Playing God (1997)",
+           43
+          ],
+          [
+           337,
+           "House of Yes, The (1997)",
+           18
+          ],
+          [
+           338,
+           "Bean (1997)",
+           91
+          ],
+          [
+           339,
+           "Mad City (1997)",
+           47
+          ],
+          [
+           340,
+           "Boogie Nights (1997)",
+           189
+          ],
+          [
+           341,
+           "Critical Care (1997)",
+           11
+          ],
+          [
+           342,
+           "Man Who Knew Too Little, The (1997)",
+           52
+          ],
+          [
+           343,
+           "Alien: Resurrection (1997)",
+           124
+          ],
+          [
+           344,
+           "Apostle, The (1997)",
+           55
+          ],
+          [
+           345,
+           "Deconstructing Harry (1997)",
+           65
+          ],
+          [
+           346,
+           "Jackie Brown (1997)",
+           126
+          ],
+          [
+           347,
+           "Wag the Dog (1997)",
+           137
+          ],
+          [
+           348,
+           "Desperate Measures (1998)",
+           27
+          ],
+          [
+           349,
+           "Hard Rain (1998)",
+           31
+          ],
+          [
+           350,
+           "Fallen (1998)",
+           41
+          ],
+          [
+           351,
+           "Prophecy II, The (1998)",
+           20
+          ],
+          [
+           352,
+           "Spice World (1997)",
+           26
+          ],
+          [
+           353,
+           "Deep Rising (1998)",
+           14
+          ],
+          [
+           354,
+           "Wedding Singer, The (1998)",
+           72
+          ],
+          [
+           355,
+           "Sphere (1998)",
+           41
+          ],
+          [
+           356,
+           "Client, The (1994)",
+           97
+          ],
+          [
+           357,
+           "One Flew Over the Cuckoo's Nest (1975)",
+           264
+          ],
+          [
+           358,
+           "Spawn (1997)",
+           143
+          ],
+          [
+           359,
+           "Assignment, The (1997)",
+           18
+          ],
+          [
+           360,
+           "Wonderland (1997)",
+           10
+          ],
+          [
+           361,
+           "Incognito (1997)",
+           10
+          ],
+          [
+           362,
+           "Blues Brothers 2000 (1998)",
+           28
+          ],
+          [
+           363,
+           "Sudden Death (1995)",
+           47
+          ],
+          [
+           364,
+           "Ace Ventura: When Nature Calls (1995)",
+           37
+          ],
+          [
+           365,
+           "Powder (1995)",
+           48
+          ],
+          [
+           366,
+           "Dangerous Minds (1995)",
+           47
+          ],
+          [
+           367,
+           "Clueless (1995)",
+           170
+          ],
+          [
+           368,
+           "Bio-Dome (1996)",
+           31
+          ],
+          [
+           369,
+           "Black Sheep (1996)",
+           55
+          ],
+          [
+           370,
+           "Mary Reilly (1996)",
+           39
+          ],
+          [
+           371,
+           "Bridges of Madison County, The (1995)",
+           67
+          ],
+          [
+           372,
+           "Jeffrey (1995)",
+           34
+          ],
+          [
+           373,
+           "Judge Dredd (1995)",
+           39
+          ],
+          [
+           374,
+           "Mighty Morphin Power Rangers: The Movie (1995)",
+           11
+          ],
+          [
+           375,
+           "Showgirls (1995)",
+           23
+          ],
+          [
+           376,
+           "Houseguest (1994)",
+           24
+          ],
+          [
+           377,
+           "Heavyweights (1994)",
+           13
+          ],
+          [
+           378,
+           "Miracle on 34th Street (1994)",
+           101
+          ],
+          [
+           379,
+           "Tales From the Crypt Presents: Demon Knight (1995)",
+           43
+          ],
+          [
+           380,
+           "Star Trek: Generations (1994)",
+           116
+          ],
+          [
+           381,
+           "Muriel's Wedding (1994)",
+           100
+          ],
+          [
+           382,
+           "Adventures of Priscilla, Queen of the Desert, The (1994)",
+           111
+          ],
+          [
+           383,
+           "Flintstones, The (1994)",
+           31
+          ],
+          [
+           384,
+           "Naked Gun 33 1/3: The Final Insult (1994)",
+           69
+          ],
+          [
+           385,
+           "True Lies (1994)",
+           208
+          ],
+          [
+           386,
+           "Addams Family Values (1993)",
+           87
+          ],
+          [
+           387,
+           "Age of Innocence, The (1993)",
+           65
+          ],
+          [
+           388,
+           "Beverly Hills Cop III (1994)",
+           28
+          ],
+          [
+           389,
+           "Black Beauty (1994)",
+           27
+          ],
+          [
+           390,
+           "Fear of a Black Hat (1993)",
+           10
+          ],
+          [
+           391,
+           "Last Action Hero (1993)",
+           59
+          ],
+          [
+           392,
+           "Man Without a Face, The (1993)",
+           68
+          ],
+          [
+           393,
+           "Mrs. Doubtfire (1993)",
+           192
+          ],
+          [
+           394,
+           "Radioland Murders (1994)",
+           12
+          ],
+          [
+           395,
+           "Robin Hood: Men in Tights (1993)",
+           56
+          ],
+          [
+           396,
+           "Serial Mom (1994)",
+           54
+          ],
+          [
+           397,
+           "Striking Distance (1993)",
+           12
+          ],
+          [
+           398,
+           "Super Mario Bros. (1993)",
+           26
+          ],
+          [
+           399,
+           "Three Musketeers, The (1993)",
+           89
+          ],
+          [
+           400,
+           "Little Rascals, The (1994)",
+           18
+          ],
+          [
+           401,
+           "Brady Bunch Movie, The (1995)",
+           76
+          ],
+          [
+           402,
+           "Ghost (1990)",
+           170
+          ],
+          [
+           403,
+           "Batman (1989)",
+           201
+          ],
+          [
+           404,
+           "Pinocchio (1940)",
+           101
+          ],
+          [
+           405,
+           "Mission: Impossible (1996)",
+           344
+          ],
+          [
+           406,
+           "Thinner (1996)",
+           49
+          ],
+          [
+           407,
+           "Spy Hard (1996)",
+           43
+          ],
+          [
+           408,
+           "Close Shave, A (1995)",
+           112
+          ],
+          [
+           409,
+           "Jack (1996)",
+           70
+          ],
+          [
+           410,
+           "Kingpin (1996)",
+           162
+          ],
+          [
+           411,
+           "Nutty Professor, The (1996)",
+           163
+          ],
+          [
+           412,
+           "Very Brady Sequel, A (1996)",
+           93
+          ],
+          [
+           413,
+           "Tales from the Crypt Presents: Bordello of Blood (1996)",
+           55
+          ],
+          [
+           414,
+           "My Favorite Year (1982)",
+           62
+          ],
+          [
+           415,
+           "Apple Dumpling Gang, The (1975)",
+           25
+          ],
+          [
+           416,
+           "Old Yeller (1957)",
+           64
+          ],
+          [
+           417,
+           "Parent Trap, The (1961)",
+           73
+          ],
+          [
+           418,
+           "Cinderella (1950)",
+           129
+          ],
+          [
+           419,
+           "Mary Poppins (1964)",
+           178
+          ],
+          [
+           420,
+           "Alice in Wonderland (1951)",
+           81
+          ],
+          [
+           421,
+           "William Shakespeare's Romeo and Juliet (1996)",
+           106
+          ],
+          [
+           422,
+           "Aladdin and the King of Thieves (1996)",
+           26
+          ],
+          [
+           423,
+           "E.T. the Extra-Terrestrial (1982)",
+           300
+          ],
+          [
+           424,
+           "Children of the Corn: The Gathering (1996)",
+           19
+          ],
+          [
+           425,
+           "Bob Roberts (1992)",
+           85
+          ],
+          [
+           426,
+           "Transformers: The Movie, The (1986)",
+           32
+          ],
+          [
+           427,
+           "To Kill a Mockingbird (1962)",
+           219
+          ],
+          [
+           428,
+           "Harold and Maude (1971)",
+           121
+          ],
+          [
+           429,
+           "Day the Earth Stood Still, The (1951)",
+           97
+          ],
+          [
+           430,
+           "Duck Soup (1933)",
+           93
+          ],
+          [
+           431,
+           "Highlander (1986)",
+           153
+          ],
+          [
+           432,
+           "Fantasia (1940)",
+           174
+          ],
+          [
+           433,
+           "Heathers (1989)",
+           171
+          ],
+          [
+           434,
+           "Forbidden Planet (1956)",
+           67
+          ],
+          [
+           435,
+           "Butch Cassidy and the Sundance Kid (1969)",
+           216
+          ],
+          [
+           436,
+           "American Werewolf in London, An (1981)",
+           99
+          ],
+          [
+           437,
+           "Amityville 1992: It's About Time (1992)",
+           5
+          ],
+          [
+           438,
+           "Amityville 3-D (1983)",
+           6
+          ],
+          [
+           439,
+           "Amityville: A New Generation (1993)",
+           5
+          ],
+          [
+           440,
+           "Amityville II: The Possession (1982)",
+           14
+          ],
+          [
+           441,
+           "Amityville Horror, The (1979)",
+           53
+          ],
+          [
+           442,
+           "Amityville Curse, The (1990)",
+           4
+          ],
+          [
+           443,
+           "Birds, The (1963)",
+           162
+          ],
+          [
+           444,
+           "Blob, The (1958)",
+           46
+          ],
+          [
+           445,
+           "Body Snatcher, The (1945)",
+           22
+          ],
+          [
+           446,
+           "Burnt Offerings (1976)",
+           9
+          ],
+          [
+           447,
+           "Carrie (1976)",
+           121
+          ],
+          [
+           448,
+           "Omen, The (1976)",
+           85
+          ],
+          [
+           449,
+           "Star Trek: The Motion Picture (1979)",
+           117
+          ],
+          [
+           450,
+           "Star Trek V: The Final Frontier (1989)",
+           63
+          ],
+          [
+           451,
+           "Grease (1978)",
+           170
+          ],
+          [
+           452,
+           "Jaws 2 (1978)",
+           66
+          ],
+          [
+           453,
+           "Jaws 3-D (1983)",
+           16
+          ],
+          [
+           454,
+           "Bastard Out of Carolina (1996)",
+           16
+          ],
+          [
+           455,
+           "Jackie Chan's First Strike (1996)",
+           145
+          ],
+          [
+           456,
+           "Beverly Hills Ninja (1997)",
+           48
+          ],
+          [
+           457,
+           "Free Willy 3: The Rescue (1997)",
+           27
+          ],
+          [
+           458,
+           "Nixon (1995)",
+           90
+          ],
+          [
+           459,
+           "Cry, the Beloved Country (1995)",
+           24
+          ],
+          [
+           460,
+           "Crossing Guard, The (1995)",
+           28
+          ],
+          [
+           461,
+           "Smoke (1995)",
+           74
+          ],
+          [
+           462,
+           "Like Water For Chocolate (Como agua para chocolate) (1992)",
+           148
+          ],
+          [
+           463,
+           "Secret of Roan Inish, The (1994)",
+           71
+          ],
+          [
+           464,
+           "Vanya on 42nd Street (1994)",
+           27
+          ],
+          [
+           465,
+           "Jungle Book, The (1994)",
+           85
+          ],
+          [
+           466,
+           "Red Rock West (1992)",
+           52
+          ],
+          [
+           467,
+           "Bronx Tale, A (1993)",
+           48
+          ],
+          [
+           468,
+           "Rudy (1993)",
+           64
+          ],
+          [
+           469,
+           "Short Cuts (1993)",
+           67
+          ],
+          [
+           470,
+           "Tombstone (1993)",
+           108
+          ],
+          [
+           471,
+           "Courage Under Fire (1996)",
+           221
+          ],
+          [
+           472,
+           "Dragonheart (1996)",
+           158
+          ],
+          [
+           473,
+           "James and the Giant Peach (1996)",
+           126
+          ],
+          [
+           474,
+           "Dr. Strangelove or: How I Learned to Stop Worrying and Love the Bomb (1963)",
+           194
+          ],
+          [
+           475,
+           "Trainspotting (1996)",
+           250
+          ],
+          [
+           476,
+           "First Wives Club, The (1996)",
+           160
+          ],
+          [
+           477,
+           "Matilda (1996)",
+           95
+          ],
+          [
+           478,
+           "Philadelphia Story, The (1940)",
+           104
+          ],
+          [
+           479,
+           "Vertigo (1958)",
+           179
+          ],
+          [
+           480,
+           "North by Northwest (1959)",
+           179
+          ],
+          [
+           481,
+           "Apartment, The (1960)",
+           63
+          ],
+          [
+           482,
+           "Some Like It Hot (1959)",
+           128
+          ],
+          [
+           483,
+           "Casablanca (1942)",
+           243
+          ],
+          [
+           484,
+           "Maltese Falcon, The (1941)",
+           138
+          ],
+          [
+           485,
+           "My Fair Lady (1964)",
+           125
+          ],
+          [
+           486,
+           "Sabrina (1954)",
+           64
+          ],
+          [
+           487,
+           "Roman Holiday (1953)",
+           68
+          ],
+          [
+           488,
+           "Sunset Blvd. (1950)",
+           65
+          ],
+          [
+           489,
+           "Notorious (1946)",
+           52
+          ],
+          [
+           490,
+           "To Catch a Thief (1955)",
+           50
+          ],
+          [
+           491,
+           "Adventures of Robin Hood, The (1938)",
+           67
+          ],
+          [
+           492,
+           "East of Eden (1955)",
+           59
+          ],
+          [
+           493,
+           "Thin Man, The (1934)",
+           60
+          ],
+          [
+           494,
+           "His Girl Friday (1940)",
+           56
+          ],
+          [
+           495,
+           "Around the World in 80 Days (1956)",
+           59
+          ],
+          [
+           496,
+           "It's a Wonderful Life (1946)",
+           231
+          ],
+          [
+           497,
+           "Bringing Up Baby (1938)",
+           68
+          ],
+          [
+           498,
+           "African Queen, The (1951)",
+           152
+          ],
+          [
+           499,
+           "Cat on a Hot Tin Roof (1958)",
+           62
+          ],
+          [
+           500,
+           "Fly Away Home (1996)",
+           31
+          ],
+          [
+           501,
+           "Dumbo (1941)",
+           123
+          ],
+          [
+           502,
+           "Bananas (1971)",
+           57
+          ],
+          [
+           503,
+           "Candidate, The (1972)",
+           39
+          ],
+          [
+           504,
+           "Bonnie and Clyde (1967)",
+           122
+          ],
+          [
+           505,
+           "Dial M for Murder (1954)",
+           68
+          ],
+          [
+           506,
+           "Rebel Without a Cause (1955)",
+           90
+          ],
+          [
+           507,
+           "Streetcar Named Desire, A (1951)",
+           98
+          ],
+          [
+           508,
+           "People vs. Larry Flynt, The (1996)",
+           215
+          ],
+          [
+           509,
+           "My Left Foot (1989)",
+           121
+          ],
+          [
+           510,
+           "Magnificent Seven, The (1954)",
+           121
+          ],
+          [
+           511,
+           "Lawrence of Arabia (1962)",
+           173
+          ],
+          [
+           512,
+           "Wings of Desire (1987)",
+           57
+          ],
+          [
+           513,
+           "Third Man, The (1949)",
+           72
+          ],
+          [
+           514,
+           "Annie Hall (1977)",
+           180
+          ],
+          [
+           515,
+           "Boot, Das (1981)",
+           201
+          ],
+          [
+           516,
+           "Local Hero (1983)",
+           63
+          ],
+          [
+           517,
+           "Manhattan (1979)",
+           91
+          ],
+          [
+           518,
+           "Miller's Crossing (1990)",
+           89
+          ],
+          [
+           519,
+           "Treasure of the Sierra Madre, The (1948)",
+           80
+          ],
+          [
+           520,
+           "Great Escape, The (1963)",
+           124
+          ],
+          [
+           521,
+           "Deer Hunter, The (1978)",
+           120
+          ],
+          [
+           522,
+           "Down by Law (1986)",
+           35
+          ],
+          [
+           523,
+           "Cool Hand Luke (1967)",
+           164
+          ],
+          [
+           524,
+           "Great Dictator, The (1940)",
+           46
+          ],
+          [
+           525,
+           "Big Sleep, The (1946)",
+           73
+          ],
+          [
+           526,
+           "Ben-Hur (1959)",
+           124
+          ],
+          [
+           527,
+           "Gandhi (1982)",
+           195
+          ],
+          [
+           528,
+           "Killing Fields, The (1984)",
+           121
+          ],
+          [
+           529,
+           "My Life as a Dog (Mitt liv som hund) (1985)",
+           93
+          ],
+          [
+           530,
+           "Man Who Would Be King, The (1975)",
+           80
+          ],
+          [
+           531,
+           "Shine (1996)",
+           129
+          ],
+          [
+           532,
+           "Kama Sutra: A Tale of Love (1996)",
+           22
+          ],
+          [
+           533,
+           "Daytrippers, The (1996)",
+           15
+          ],
+          [
+           534,
+           "Traveller (1997)",
+           13
+          ],
+          [
+           535,
+           "Addicted to Love (1997)",
+           54
+          ],
+          [
+           536,
+           "Ponette (1996)",
+           10
+          ],
+          [
+           537,
+           "My Own Private Idaho (1991)",
+           30
+          ],
+          [
+           538,
+           "Anastasia (1997)",
+           66
+          ],
+          [
+           539,
+           "Mouse Hunt (1997)",
+           44
+          ],
+          [
+           540,
+           "Money Train (1995)",
+           43
+          ],
+          [
+           541,
+           "Mortal Kombat (1995)",
+           49
+          ],
+          [
+           542,
+           "Pocahontas (1995)",
+           51
+          ],
+          [
+           543,
+           "Misérables, Les (1995)",
+           21
+          ],
+          [
+           544,
+           "Things to Do in Denver when You're Dead (1995)",
+           71
+          ],
+          [
+           545,
+           "Vampire in Brooklyn (1995)",
+           12
+          ],
+          [
+           546,
+           "Broken Arrow (1996)",
+           254
+          ],
+          [
+           547,
+           "Young Poisoner's Handbook, The (1995)",
+           41
+          ],
+          [
+           548,
+           "NeverEnding Story III, The (1994)",
+           12
+          ],
+          [
+           549,
+           "Rob Roy (1995)",
+           92
+          ],
+          [
+           550,
+           "Die Hard: With a Vengeance (1995)",
+           151
+          ],
+          [
+           551,
+           "Lord of Illusions (1995)",
+           24
+          ],
+          [
+           552,
+           "Species (1995)",
+           45
+          ],
+          [
+           553,
+           "Walk in the Clouds, A (1995)",
+           63
+          ],
+          [
+           554,
+           "Waterworld (1995)",
+           102
+          ],
+          [
+           555,
+           "White Man's Burden (1995)",
+           10
+          ],
+          [
+           556,
+           "Wild Bill (1995)",
+           12
+          ],
+          [
+           557,
+           "Farinelli: il castrato (1994)",
+           17
+          ],
+          [
+           558,
+           "Heavenly Creatures (1994)",
+           70
+          ],
+          [
+           559,
+           "Interview with the Vampire (1994)",
+           137
+          ],
+          [
+           560,
+           "Kid in King Arthur's Court, A (1995)",
+           22
+          ],
+          [
+           561,
+           "Mary Shelley's Frankenstein (1994)",
+           59
+          ],
+          [
+           562,
+           "Quick and the Dead, The (1995)",
+           48
+          ],
+          [
+           563,
+           "Stephen King's The Langoliers (1995)",
+           29
+          ],
+          [
+           564,
+           "Tales from the Hood (1995)",
+           27
+          ],
+          [
+           565,
+           "Village of the Damned (1995)",
+           22
+          ],
+          [
+           566,
+           "Clear and Present Danger (1994)",
+           179
+          ],
+          [
+           567,
+           "Wes Craven's New Nightmare (1994)",
+           35
+          ],
+          [
+           568,
+           "Speed (1994)",
+           230
+          ],
+          [
+           569,
+           "Wolf (1994)",
+           67
+          ],
+          [
+           570,
+           "Wyatt Earp (1994)",
+           50
+          ],
+          [
+           571,
+           "Another Stakeout (1993)",
+           28
+          ],
+          [
+           572,
+           "Blown Away (1994)",
+           29
+          ],
+          [
+           573,
+           "Body Snatchers (1993)",
+           33
+          ],
+          [
+           574,
+           "Boxing Helena (1993)",
+           15
+          ],
+          [
+           575,
+           "City Slickers II: The Legend of Curly's Gold (1994)",
+           44
+          ],
+          [
+           576,
+           "Cliffhanger (1993)",
+           93
+          ],
+          [
+           577,
+           "Coneheads (1993)",
+           41
+          ],
+          [
+           578,
+           "Demolition Man (1993)",
+           92
+          ],
+          [
+           579,
+           "Fatal Instinct (1993)",
+           19
+          ],
+          [
+           580,
+           "Englishman Who Went Up a Hill, But Came Down a Mountain, The (1995)",
+           32
+          ],
+          [
+           581,
+           "Kalifornia (1993)",
+           59
+          ],
+          [
+           582,
+           "Piano, The (1993)",
+           168
+          ],
+          [
+           583,
+           "Romeo Is Bleeding (1993)",
+           37
+          ],
+          [
+           584,
+           "Secret Garden, The (1993)",
+           79
+          ],
+          [
+           585,
+           "Son in Law (1993)",
+           39
+          ],
+          [
+           586,
+           "Terminal Velocity (1994)",
+           34
+          ],
+          [
+           587,
+           "Hour of the Pig, The (1993)",
+           14
+          ],
+          [
+           588,
+           "Beauty and the Beast (1991)",
+           202
+          ],
+          [
+           589,
+           "Wild Bunch, The (1969)",
+           43
+          ],
+          [
+           590,
+           "Hellraiser: Bloodline (1996)",
+           18
+          ],
+          [
+           591,
+           "Primal Fear (1996)",
+           178
+          ],
+          [
+           592,
+           "True Crime (1995)",
+           9
+          ],
+          [
+           593,
+           "Stalingrad (1993)",
+           12
+          ],
+          [
+           594,
+           "Heavy (1995)",
+           5
+          ],
+          [
+           595,
+           "Fan, The (1996)",
+           64
+          ],
+          [
+           596,
+           "Hunchback of Notre Dame, The (1996)",
+           127
+          ],
+          [
+           597,
+           "Eraser (1996)",
+           206
+          ],
+          [
+           598,
+           "Big Squeeze, The (1996)",
+           4
+          ],
+          [
+           599,
+           "Police Story 4: Project S (Chao ji ji hua) (1993)",
+           1
+          ],
+          [
+           600,
+           "Daniel Defoe's Robinson Crusoe (1996)",
+           2
+          ],
+          [
+           601,
+           "For Whom the Bell Tolls (1943)",
+           20
+          ],
+          [
+           602,
+           "American in Paris, An (1951)",
+           50
+          ],
+          [
+           603,
+           "Rear Window (1954)",
+           209
+          ],
+          [
+           604,
+           "It Happened One Night (1934)",
+           81
+          ],
+          [
+           605,
+           "Meet Me in St. Louis (1944)",
+           31
+          ],
+          [
+           606,
+           "All About Eve (1950)",
+           66
+          ],
+          [
+           607,
+           "Rebecca (1940)",
+           66
+          ],
+          [
+           608,
+           "Spellbound (1945)",
+           30
+          ],
+          [
+           609,
+           "Father of the Bride (1950)",
+           60
+          ],
+          [
+           610,
+           "Gigi (1958)",
+           41
+          ],
+          [
+           611,
+           "Laura (1944)",
+           40
+          ],
+          [
+           612,
+           "Lost Horizon (1937)",
+           34
+          ],
+          [
+           613,
+           "My Man Godfrey (1936)",
+           27
+          ],
+          [
+           614,
+           "Giant (1956)",
+           51
+          ],
+          [
+           615,
+           "39 Steps, The (1935)",
+           59
+          ],
+          [
+           616,
+           "Night of the Living Dead (1968)",
+           64
+          ],
+          [
+           617,
+           "Blue Angel, The (Blaue Engel, Der) (1930)",
+           18
+          ],
+          [
+           618,
+           "Picnic (1955)",
+           18
+          ],
+          [
+           619,
+           "Extreme Measures (1996)",
+           64
+          ],
+          [
+           620,
+           "Chamber, The (1996)",
+           43
+          ],
+          [
+           621,
+           "Davy Crockett, King of the Wild Frontier (1955)",
+           11
+          ],
+          [
+           622,
+           "Swiss Family Robinson (1960)",
+           39
+          ],
+          [
+           623,
+           "Angels in the Outfield (1994)",
+           39
+          ],
+          [
+           624,
+           "Three Caballeros, The (1945)",
+           22
+          ],
+          [
+           625,
+           "Sword in the Stone, The (1963)",
+           82
+          ],
+          [
+           626,
+           "So Dear to My Heart (1949)",
+           4
+          ],
+          [
+           627,
+           "Robin Hood: Prince of Thieves (1991)",
+           75
+          ],
+          [
+           628,
+           "Sleepers (1996)",
+           169
+          ],
+          [
+           629,
+           "Victor/Victoria (1982)",
+           77
+          ],
+          [
+           630,
+           "Great Race, The (1965)",
+           31
+          ],
+          [
+           631,
+           "Crying Game, The (1992)",
+           119
+          ],
+          [
+           632,
+           "Sophie's Choice (1982)",
+           58
+          ],
+          [
+           633,
+           "Christmas Carol, A (1938)",
+           69
+          ],
+          [
+           634,
+           "Microcosmos: Le peuple de l'herbe (1996)",
+           24
+          ],
+          [
+           635,
+           "Fog, The (1980)",
+           23
+          ],
+          [
+           636,
+           "Escape from New York (1981)",
+           91
+          ],
+          [
+           637,
+           "Howling, The (1981)",
+           38
+          ],
+          [
+           638,
+           "Return of Martin Guerre, The (Retour de Martin Guerre, Le) (1982)",
+           44
+          ],
+          [
+           639,
+           "Tin Drum, The (Blechtrommel, Die) (1979)",
+           40
+          ],
+          [
+           640,
+           "Cook the Thief His Wife & Her Lover, The (1989)",
+           82
+          ],
+          [
+           641,
+           "Paths of Glory (1957)",
+           33
+          ],
+          [
+           642,
+           "Grifters, The (1990)",
+           89
+          ],
+          [
+           643,
+           "The Innocent (1994)",
+           4
+          ],
+          [
+           644,
+           "Thin Blue Line, The (1988)",
+           35
+          ],
+          [
+           645,
+           "Paris Is Burning (1990)",
+           27
+          ],
+          [
+           646,
+           "Once Upon a Time in the West (1969)",
+           38
+          ],
+          [
+           647,
+           "Ran (1985)",
+           70
+          ],
+          [
+           648,
+           "Quiet Man, The (1952)",
+           67
+          ],
+          [
+           649,
+           "Once Upon a Time in America (1984)",
+           50
+          ],
+          [
+           650,
+           "Seventh Seal, The (Sjunde inseglet, Det) (1957)",
+           72
+          ],
+          [
+           651,
+           "Glory (1989)",
+           171
+          ],
+          [
+           652,
+           "Rosencrantz and Guildenstern Are Dead (1990)",
+           90
+          ],
+          [
+           653,
+           "Touch of Evil (1958)",
+           34
+          ],
+          [
+           654,
+           "Chinatown (1974)",
+           147
+          ],
+          [
+           655,
+           "Stand by Me (1986)",
+           227
+          ],
+          [
+           656,
+           "M (1931)",
+           44
+          ],
+          [
+           657,
+           "Manchurian Candidate, The (1962)",
+           131
+          ],
+          [
+           658,
+           "Pump Up the Volume (1990)",
+           79
+          ],
+          [
+           659,
+           "Arsenic and Old Lace (1944)",
+           115
+          ],
+          [
+           660,
+           "Fried Green Tomatoes (1991)",
+           153
+          ],
+          [
+           661,
+           "High Noon (1952)",
+           88
+          ],
+          [
+           662,
+           "Somewhere in Time (1980)",
+           82
+          ],
+          [
+           663,
+           "Being There (1979)",
+           116
+          ],
+          [
+           664,
+           "Paris, Texas (1984)",
+           46
+          ],
+          [
+           665,
+           "Alien 3 (1992)",
+           100
+          ],
+          [
+           666,
+           "Blood For Dracula (Andy Warhol's Dracula) (1974)",
+           5
+          ],
+          [
+           667,
+           "Audrey Rose (1977)",
+           12
+          ],
+          [
+           668,
+           "Blood Beach (1981)",
+           6
+          ],
+          [
+           669,
+           "Body Parts (1991)",
+           13
+          ],
+          [
+           670,
+           "Body Snatchers (1993)",
+           36
+          ],
+          [
+           671,
+           "Bride of Frankenstein (1935)",
+           46
+          ],
+          [
+           672,
+           "Candyman (1992)",
+           65
+          ],
+          [
+           673,
+           "Cape Fear (1962)",
+           86
+          ],
+          [
+           674,
+           "Cat People (1982)",
+           48
+          ],
+          [
+           675,
+           "Nosferatu (Nosferatu, eine Symphonie des Grauens) (1922)",
+           54
+          ],
+          [
+           676,
+           "Crucible, The (1996)",
+           77
+          ],
+          [
+           677,
+           "Fire on the Mountain (1996)",
+           1
+          ],
+          [
+           678,
+           "Volcano (1997)",
+           219
+          ],
+          [
+           679,
+           "Conan the Barbarian (1981)",
+           107
+          ],
+          [
+           680,
+           "Kull the Conqueror (1997)",
+           34
+          ],
+          [
+           681,
+           "Wishmaster (1997)",
+           27
+          ],
+          [
+           682,
+           "I Know What You Did Last Summer (1997)",
+           100
+          ],
+          [
+           683,
+           "Rocket Man (1997)",
+           49
+          ],
+          [
+           684,
+           "In the Line of Fire (1993)",
+           169
+          ],
+          [
+           685,
+           "Executive Decision (1996)",
+           157
+          ],
+          [
+           686,
+           "Perfect World, A (1993)",
+           50
+          ],
+          [
+           687,
+           "McHale's Navy (1997)",
+           69
+          ],
+          [
+           688,
+           "Leave It to Beaver (1997)",
+           44
+          ],
+          [
+           689,
+           "Jackal, The (1997)",
+           87
+          ],
+          [
+           690,
+           "Seven Years in Tibet (1997)",
+           155
+          ],
+          [
+           691,
+           "Dark City (1998)",
+           16
+          ],
+          [
+           692,
+           "American President, The (1995)",
+           164
+          ],
+          [
+           693,
+           "Casino (1995)",
+           91
+          ],
+          [
+           694,
+           "Persuasion (1995)",
+           44
+          ],
+          [
+           695,
+           "Kicking and Screaming (1995)",
+           13
+          ],
+          [
+           696,
+           "City Hall (1996)",
+           79
+          ],
+          [
+           697,
+           "Basketball Diaries, The (1995)",
+           40
+          ],
+          [
+           698,
+           "Browning Version, The (1994)",
+           10
+          ],
+          [
+           699,
+           "Little Women (1994)",
+           102
+          ],
+          [
+           700,
+           "Miami Rhapsody (1995)",
+           15
+          ],
+          [
+           701,
+           "Wonderful, Horrible Life of Leni Riefenstahl, The (1993)",
+           10
+          ],
+          [
+           702,
+           "Barcelona (1994)",
+           53
+          ],
+          [
+           703,
+           "Widows' Peak (1994)",
+           19
+          ],
+          [
+           704,
+           "House of the Spirits, The (1993)",
+           24
+          ],
+          [
+           705,
+           "Singin' in the Rain (1952)",
+           137
+          ],
+          [
+           706,
+           "Bad Moon (1996)",
+           6
+          ],
+          [
+           707,
+           "Enchanted April (1991)",
+           70
+          ],
+          [
+           708,
+           "Sex, Lies, and Videotape (1989)",
+           101
+          ],
+          [
+           709,
+           "Strictly Ballroom (1992)",
+           104
+          ],
+          [
+           710,
+           "Better Off Dead... (1985)",
+           79
+          ],
+          [
+           711,
+           "Substance of Fire, The (1996)",
+           1
+          ],
+          [
+           712,
+           "Tin Men (1987)",
+           51
+          ],
+          [
+           713,
+           "Othello (1995)",
+           72
+          ],
+          [
+           714,
+           "Carrington (1995)",
+           13
+          ],
+          [
+           715,
+           "To Die For (1995)",
+           87
+          ],
+          [
+           716,
+           "Home for the Holidays (1995)",
+           58
+          ],
+          [
+           717,
+           "Juror, The (1996)",
+           82
+          ],
+          [
+           718,
+           "In the Bleak Midwinter (1995)",
+           16
+          ],
+          [
+           719,
+           "Canadian Bacon (1994)",
+           29
+          ],
+          [
+           720,
+           "First Knight (1995)",
+           86
+          ],
+          [
+           721,
+           "Mallrats (1995)",
+           54
+          ],
+          [
+           722,
+           "Nine Months (1995)",
+           58
+          ],
+          [
+           723,
+           "Boys on the Side (1995)",
+           34
+          ],
+          [
+           724,
+           "Circle of Friends (1995)",
+           76
+          ],
+          [
+           725,
+           "Exit to Eden (1994)",
+           16
+          ],
+          [
+           726,
+           "Fluke (1995)",
+           14
+          ],
+          [
+           727,
+           "Immortal Beloved (1994)",
+           63
+          ],
+          [
+           728,
+           "Junior (1994)",
+           45
+          ],
+          [
+           729,
+           "Nell (1994)",
+           81
+          ],
+          [
+           730,
+           "Queen Margot (Reine Margot, La) (1994)",
+           24
+          ],
+          [
+           731,
+           "Corrina, Corrina (1994)",
+           39
+          ],
+          [
+           732,
+           "Dave (1993)",
+           180
+          ],
+          [
+           733,
+           "Go Fish (1994)",
+           15
+          ],
+          [
+           734,
+           "Made in America (1993)",
+           27
+          ],
+          [
+           735,
+           "Philadelphia (1993)",
+           137
+          ],
+          [
+           736,
+           "Shadowlands (1993)",
+           78
+          ],
+          [
+           737,
+           "Sirens (1994)",
+           59
+          ],
+          [
+           738,
+           "Threesome (1994)",
+           31
+          ],
+          [
+           739,
+           "Pretty Woman (1990)",
+           164
+          ],
+          [
+           740,
+           "Jane Eyre (1996)",
+           63
+          ],
+          [
+           741,
+           "Last Supper, The (1995)",
+           58
+          ],
+          [
+           742,
+           "Ransom (1996)",
+           267
+          ],
+          [
+           743,
+           "Crow: City of Angels, The (1996)",
+           39
+          ],
+          [
+           744,
+           "Michael Collins (1996)",
+           92
+          ],
+          [
+           745,
+           "Ruling Class, The (1972)",
+           16
+          ],
+          [
+           746,
+           "Real Genius (1985)",
+           119
+          ],
+          [
+           747,
+           "Benny & Joon (1993)",
+           102
+          ],
+          [
+           748,
+           "Saint, The (1997)",
+           316
+          ],
+          [
+           749,
+           "MatchMaker, The (1997)",
+           51
+          ],
+          [
+           750,
+           "Amistad (1997)",
+           124
+          ],
+          [
+           751,
+           "Tomorrow Never Dies (1997)",
+           180
+          ],
+          [
+           752,
+           "Replacement Killers, The (1998)",
+           39
+          ],
+          [
+           753,
+           "Burnt By the Sun (1994)",
+           24
+          ],
+          [
+           754,
+           "Red Corner (1997)",
+           57
+          ],
+          [
+           755,
+           "Jumanji (1995)",
+           96
+          ],
+          [
+           756,
+           "Father of the Bride Part II (1995)",
+           128
+          ],
+          [
+           757,
+           "Across the Sea of Time (1995)",
+           4
+          ],
+          [
+           758,
+           "Lawnmower Man 2: Beyond Cyberspace (1996)",
+           21
+          ],
+          [
+           759,
+           "Fair Game (1995)",
+           11
+          ],
+          [
+           760,
+           "Screamers (1995)",
+           46
+          ],
+          [
+           761,
+           "Nick of Time (1995)",
+           44
+          ],
+          [
+           762,
+           "Beautiful Girls (1996)",
+           115
+          ],
+          [
+           763,
+           "Happy Gilmore (1996)",
+           149
+          ],
+          [
+           764,
+           "If Lucy Fell (1996)",
+           29
+          ],
+          [
+           765,
+           "Boomerang (1992)",
+           32
+          ],
+          [
+           766,
+           "Man of the Year (1995)",
+           9
+          ],
+          [
+           767,
+           "Addiction, The (1995)",
+           11
+          ],
+          [
+           768,
+           "Casper (1995)",
+           52
+          ],
+          [
+           769,
+           "Congo (1995)",
+           42
+          ],
+          [
+           770,
+           "Devil in a Blue Dress (1995)",
+           57
+          ],
+          [
+           771,
+           "Johnny Mnemonic (1995)",
+           41
+          ],
+          [
+           772,
+           "Kids (1995)",
+           49
+          ],
+          [
+           773,
+           "Mute Witness (1994)",
+           17
+          ],
+          [
+           774,
+           "Prophecy, The (1995)",
+           32
+          ],
+          [
+           775,
+           "Something to Talk About (1995)",
+           26
+          ],
+          [
+           776,
+           "Three Wishes (1995)",
+           9
+          ],
+          [
+           777,
+           "Castle Freak (1995)",
+           4
+          ],
+          [
+           778,
+           "Don Juan DeMarco (1995)",
+           76
+          ],
+          [
+           779,
+           "Drop Zone (1994)",
+           31
+          ],
+          [
+           780,
+           "Dumb & Dumber (1994)",
+           69
+          ],
+          [
+           781,
+           "French Kiss (1995)",
+           84
+          ],
+          [
+           782,
+           "Little Odessa (1994)",
+           10
+          ],
+          [
+           783,
+           "Milk Money (1994)",
+           37
+          ],
+          [
+           784,
+           "Beyond Bedlam (1993)",
+           2
+          ],
+          [
+           785,
+           "Only You (1994)",
+           39
+          ],
+          [
+           786,
+           "Perez Family, The (1995)",
+           14
+          ],
+          [
+           787,
+           "Roommates (1995)",
+           13
+          ],
+          [
+           788,
+           "Relative Fear (1994)",
+           3
+          ],
+          [
+           789,
+           "Swimming with Sharks (1995)",
+           47
+          ],
+          [
+           790,
+           "Tommy Boy (1995)",
+           66
+          ],
+          [
+           791,
+           "Baby-Sitters Club, The (1995)",
+           10
+          ],
+          [
+           792,
+           "Bullets Over Broadway (1994)",
+           86
+          ],
+          [
+           793,
+           "Crooklyn (1994)",
+           10
+          ],
+          [
+           794,
+           "It Could Happen to You (1994)",
+           46
+          ],
+          [
+           795,
+           "Richie Rich (1994)",
+           21
+          ],
+          [
+           796,
+           "Speechless (1994)",
+           36
+          ],
+          [
+           797,
+           "Timecop (1994)",
+           31
+          ],
+          [
+           798,
+           "Bad Company (1995)",
+           9
+          ],
+          [
+           799,
+           "Boys Life (1995)",
+           5
+          ],
+          [
+           800,
+           "In the Mouth of Madness (1995)",
+           26
+          ],
+          [
+           801,
+           "Air Up There, The (1994)",
+           16
+          ],
+          [
+           802,
+           "Hard Target (1993)",
+           40
+          ],
+          [
+           803,
+           "Heaven & Earth (1993)",
+           9
+          ],
+          [
+           804,
+           "Jimmy Hollywood (1994)",
+           8
+          ],
+          [
+           805,
+           "Manhattan Murder Mystery (1993)",
+           27
+          ],
+          [
+           806,
+           "Menace II Society (1993)",
+           50
+          ],
+          [
+           807,
+           "Poetic Justice (1993)",
+           9
+          ],
+          [
+           808,
+           "Program, The (1993)",
+           31
+          ],
+          [
+           809,
+           "Rising Sun (1993)",
+           43
+          ],
+          [
+           810,
+           "Shadow, The (1994)",
+           45
+          ],
+          [
+           811,
+           "Thirty-Two Short Films About Glenn Gould (1993)",
+           18
+          ],
+          [
+           812,
+           "Andre (1994)",
+           18
+          ],
+          [
+           813,
+           "Celluloid Closet, The (1995)",
+           56
+          ],
+          [
+           814,
+           "Great Day in Harlem, A (1994)",
+           1
+          ],
+          [
+           815,
+           "One Fine Day (1996)",
+           112
+          ],
+          [
+           816,
+           "Candyman: Farewell to the Flesh (1995)",
+           21
+          ],
+          [
+           817,
+           "Frisk (1995)",
+           3
+          ],
+          [
+           818,
+           "Girl 6 (1996)",
+           25
+          ],
+          [
+           819,
+           "Eddie (1996)",
+           40
+          ],
+          [
+           820,
+           "Space Jam (1996)",
+           93
+          ],
+          [
+           821,
+           "Mrs. Winterbourne (1996)",
+           22
+          ],
+          [
+           822,
+           "Faces (1968)",
+           4
+          ],
+          [
+           823,
+           "Mulholland Falls (1996)",
+           82
+          ],
+          [
+           824,
+           "Great White Hype, The (1996)",
+           49
+          ],
+          [
+           825,
+           "Arrival, The (1996)",
+           83
+          ],
+          [
+           826,
+           "Phantom, The (1996)",
+           80
+          ],
+          [
+           827,
+           "Daylight (1996)",
+           57
+          ],
+          [
+           828,
+           "Alaska (1996)",
+           13
+          ],
+          [
+           829,
+           "Fled (1996)",
+           34
+          ],
+          [
+           830,
+           "Power 98 (1995)",
+           1
+          ],
+          [
+           831,
+           "Escape from L.A. (1996)",
+           91
+          ],
+          [
+           832,
+           "Bogus (1996)",
+           22
+          ],
+          [
+           833,
+           "Bulletproof (1996)",
+           49
+          ],
+          [
+           834,
+           "Halloween: The Curse of Michael Myers (1995)",
+           25
+          ],
+          [
+           835,
+           "Gay Divorcee, The (1934)",
+           15
+          ],
+          [
+           836,
+           "Ninotchka (1939)",
+           26
+          ],
+          [
+           837,
+           "Meet John Doe (1941)",
+           25
+          ],
+          [
+           838,
+           "In the Line of Duty 2 (1987)",
+           4
+          ],
+          [
+           839,
+           "Loch Ness (1995)",
+           4
+          ],
+          [
+           840,
+           "Last Man Standing (1996)",
+           53
+          ],
+          [
+           841,
+           "Glimmer Man, The (1996)",
+           48
+          ],
+          [
+           842,
+           "Pollyanna (1960)",
+           27
+          ],
+          [
+           843,
+           "Shaggy Dog, The (1959)",
+           30
+          ],
+          [
+           844,
+           "Freeway (1996)",
+           42
+          ],
+          [
+           845,
+           "That Thing You Do! (1996)",
+           176
+          ],
+          [
+           846,
+           "To Gillian on Her 37th Birthday (1996)",
+           44
+          ],
+          [
+           847,
+           "Looking for Richard (1996)",
+           55
+          ],
+          [
+           848,
+           "Murder, My Sweet (1944)",
+           9
+          ],
+          [
+           849,
+           "Days of Thunder (1990)",
+           53
+          ],
+          [
+           850,
+           "Perfect Candidate, A (1996)",
+           4
+          ],
+          [
+           851,
+           "Two or Three Things I Know About Her (1966)",
+           4
+          ],
+          [
+           852,
+           "Bloody Child, The (1996)",
+           1
+          ],
+          [
+           853,
+           "Braindead (1992)",
+           14
+          ],
+          [
+           854,
+           "Bad Taste (1987)",
+           16
+          ],
+          [
+           855,
+           "Diva (1981)",
+           66
+          ],
+          [
+           856,
+           "Night on Earth (1991)",
+           36
+          ],
+          [
+           857,
+           "Paris Was a Woman (1995)",
+           1
+          ],
+          [
+           858,
+           "Amityville: Dollhouse (1996)",
+           3
+          ],
+          [
+           859,
+           "April Fool's Day (1986)",
+           15
+          ],
+          [
+           860,
+           "Believers, The (1987)",
+           16
+          ],
+          [
+           861,
+           "Nosferatu a Venezia (1986)",
+           3
+          ],
+          [
+           862,
+           "Jingle All the Way (1996)",
+           18
+          ],
+          [
+           863,
+           "Garden of Finzi-Contini, The (Giardino dei Finzi-Contini, Il) (1970)",
+           24
+          ],
+          [
+           864,
+           "My Fellow Americans (1996)",
+           86
+          ],
+          [
+           865,
+           "Ice Storm, The (1997)",
+           21
+          ],
+          [
+           866,
+           "Michael (1996)",
+           119
+          ],
+          [
+           867,
+           "Whole Wide World, The (1996)",
+           6
+          ],
+          [
+           868,
+           "Hearts and Minds (1996)",
+           5
+          ],
+          [
+           869,
+           "Fools Rush In (1997)",
+           24
+          ],
+          [
+           870,
+           "Touch (1997)",
+           9
+          ],
+          [
+           871,
+           "Vegas Vacation (1997)",
+           75
+          ],
+          [
+           872,
+           "Love Jones (1997)",
+           42
+          ],
+          [
+           873,
+           "Picture Perfect (1997)",
+           81
+          ],
+          [
+           874,
+           "Career Girls (1997)",
+           39
+          ],
+          [
+           875,
+           "She's So Lovely (1997)",
+           53
+          ],
+          [
+           876,
+           "Money Talks (1997)",
+           47
+          ],
+          [
+           877,
+           "Excess Baggage (1997)",
+           52
+          ],
+          [
+           878,
+           "That Darn Cat! (1997)",
+           33
+          ],
+          [
+           879,
+           "Peacemaker, The (1997)",
+           136
+          ],
+          [
+           880,
+           "Soul Food (1997)",
+           59
+          ],
+          [
+           881,
+           "Money Talks (1997)",
+           45
+          ],
+          [
+           882,
+           "Washington Square (1997)",
+           34
+          ],
+          [
+           883,
+           "Telling Lies in America (1997)",
+           13
+          ],
+          [
+           884,
+           "Year of the Horse (1997)",
+           7
+          ],
+          [
+           885,
+           "Phantoms (1998)",
+           13
+          ],
+          [
+           886,
+           "Life Less Ordinary, A (1997)",
+           53
+          ],
+          [
+           887,
+           "Eve's Bayou (1997)",
+           64
+          ],
+          [
+           888,
+           "One Night Stand (1997)",
+           15
+          ],
+          [
+           889,
+           "Tango Lesson, The (1997)",
+           13
+          ],
+          [
+           890,
+           "Mortal Kombat: Annihilation (1997)",
+           43
+          ],
+          [
+           891,
+           "Bent (1997)",
+           6
+          ],
+          [
+           892,
+           "Flubber (1997)",
+           53
+          ],
+          [
+           893,
+           "For Richer or Poorer (1997)",
+           14
+          ],
+          [
+           894,
+           "Home Alone 3 (1997)",
+           19
+          ],
+          [
+           895,
+           "Scream 2 (1997)",
+           106
+          ],
+          [
+           896,
+           "Sweet Hereafter, The (1997)",
+           44
+          ],
+          [
+           897,
+           "Time Tracers (1995)",
+           2
+          ],
+          [
+           898,
+           "Postman, The (1997)",
+           58
+          ],
+          [
+           899,
+           "Winter Guest, The (1997)",
+           9
+          ],
+          [
+           900,
+           "Kundun (1997)",
+           42
+          ],
+          [
+           901,
+           "Mr. Magoo (1997)",
+           12
+          ],
+          [
+           902,
+           "Big Lebowski, The (1998)",
+           42
+          ],
+          [
+           903,
+           "Afterglow (1997)",
+           18
+          ],
+          [
+           904,
+           "Ma vie en rose (My Life in Pink) (1997)",
+           20
+          ],
+          [
+           905,
+           "Great Expectations (1998)",
+           27
+          ],
+          [
+           906,
+           "Oscar & Lucinda (1997)",
+           21
+          ],
+          [
+           907,
+           "Vermin (1998)",
+           2
+          ],
+          [
+           908,
+           "Half Baked (1998)",
+           20
+          ],
+          [
+           909,
+           "Dangerous Beauty (1998)",
+           13
+          ],
+          [
+           910,
+           "Nil By Mouth (1997)",
+           4
+          ],
+          [
+           911,
+           "Twilight (1998)",
+           4
+          ],
+          [
+           912,
+           "U.S. Marshalls (1998)",
+           9
+          ],
+          [
+           913,
+           "Love and Death on Long Island (1997)",
+           2
+          ],
+          [
+           914,
+           "Wild Things (1998)",
+           11
+          ],
+          [
+           915,
+           "Primary Colors (1998)",
+           13
+          ],
+          [
+           916,
+           "Lost in Space (1998)",
+           18
+          ],
+          [
+           917,
+           "Mercury Rising (1998)",
+           7
+          ],
+          [
+           918,
+           "City of Angels (1998)",
+           8
+          ],
+          [
+           919,
+           "City of Lost Children, The (1995)",
+           96
+          ],
+          [
+           920,
+           "Two Bits (1995)",
+           5
+          ],
+          [
+           921,
+           "Farewell My Concubine (1993)",
+           46
+          ],
+          [
+           922,
+           "Dead Man (1995)",
+           34
+          ],
+          [
+           923,
+           "Raise the Red Lantern (1991)",
+           58
+          ],
+          [
+           924,
+           "White Squall (1996)",
+           85
+          ],
+          [
+           925,
+           "Unforgettable (1996)",
+           34
+          ],
+          [
+           926,
+           "Down Periscope (1996)",
+           101
+          ],
+          [
+           927,
+           "Flower of My Secret, The (Flor de mi secreto, La) (1995)",
+           6
+          ],
+          [
+           928,
+           "Craft, The (1996)",
+           104
+          ],
+          [
+           929,
+           "Harriet the Spy (1996)",
+           40
+          ],
+          [
+           930,
+           "Chain Reaction (1996)",
+           80
+          ],
+          [
+           931,
+           "Island of Dr. Moreau, The (1996)",
+           57
+          ],
+          [
+           932,
+           "First Kid (1996)",
+           40
+          ],
+          [
+           933,
+           "Funeral, The (1996)",
+           21
+          ],
+          [
+           934,
+           "Preacher's Wife, The (1996)",
+           68
+          ],
+          [
+           935,
+           "Paradise Road (1997)",
+           7
+          ],
+          [
+           936,
+           "Brassed Off (1996)",
+           32
+          ],
+          [
+           937,
+           "Thousand Acres, A (1997)",
+           37
+          ],
+          [
+           938,
+           "Smile Like Yours, A (1997)",
+           25
+          ],
+          [
+           939,
+           "Murder in the First (1995)",
+           60
+          ],
+          [
+           940,
+           "Airheads (1994)",
+           32
+          ],
+          [
+           941,
+           "With Honors (1994)",
+           46
+          ],
+          [
+           942,
+           "What's Love Got to Do with It (1993)",
+           45
+          ],
+          [
+           943,
+           "Killing Zoe (1994)",
+           40
+          ],
+          [
+           944,
+           "Renaissance Man (1994)",
+           43
+          ],
+          [
+           945,
+           "Charade (1963)",
+           40
+          ],
+          [
+           946,
+           "Fox and the Hound, The (1981)",
+           61
+          ],
+          [
+           947,
+           "Big Blue, The (Grand bleu, Le) (1988)",
+           17
+          ],
+          [
+           948,
+           "Booty Call (1997)",
+           48
+          ],
+          [
+           949,
+           "How to Make an American Quilt (1995)",
+           71
+          ],
+          [
+           950,
+           "Georgia (1995)",
+           30
+          ],
+          [
+           951,
+           "Indian in the Cupboard, The (1995)",
+           39
+          ],
+          [
+           952,
+           "Blue in the Face (1995)",
+           45
+          ],
+          [
+           953,
+           "Unstrung Heroes (1995)",
+           22
+          ],
+          [
+           954,
+           "Unzipped (1995)",
+           11
+          ],
+          [
+           955,
+           "Before Sunrise (1995)",
+           49
+          ],
+          [
+           956,
+           "Nobody's Fool (1994)",
+           46
+          ],
+          [
+           957,
+           "Pushing Hands (1992)",
+           2
+          ],
+          [
+           958,
+           "To Live (Huozhe) (1994)",
+           14
+          ],
+          [
+           959,
+           "Dazed and Confused (1993)",
+           64
+          ],
+          [
+           960,
+           "Naked (1993)",
+           25
+          ],
+          [
+           961,
+           "Orlando (1993)",
+           34
+          ],
+          [
+           962,
+           "Ruby in Paradise (1993)",
+           23
+          ],
+          [
+           963,
+           "Some Folks Call It a Sling Blade (1993)",
+           41
+          ],
+          [
+           964,
+           "Month by the Lake, A (1995)",
+           9
+          ],
+          [
+           965,
+           "Funny Face (1957)",
+           21
+          ],
+          [
+           966,
+           "Affair to Remember, An (1957)",
+           26
+          ],
+          [
+           967,
+           "Little Lord Fauntleroy (1936)",
+           12
+          ],
+          [
+           968,
+           "Inspector General, The (1949)",
+           18
+          ],
+          [
+           969,
+           "Winnie the Pooh and the Blustery Day (1968)",
+           75
+          ],
+          [
+           970,
+           "Hear My Song (1991)",
+           8
+          ],
+          [
+           971,
+           "Mediterraneo (1991)",
+           34
+          ],
+          [
+           972,
+           "Passion Fish (1992)",
+           28
+          ],
+          [
+           973,
+           "Grateful Dead (1995)",
+           4
+          ],
+          [
+           974,
+           "Eye for an Eye (1996)",
+           32
+          ],
+          [
+           975,
+           "Fear (1996)",
+           44
+          ],
+          [
+           976,
+           "Solo (1996)",
+           12
+          ],
+          [
+           977,
+           "Substitute, The (1996)",
+           49
+          ],
+          [
+           978,
+           "Heaven's Prisoners (1996)",
+           27
+          ],
+          [
+           979,
+           "Trigger Effect, The (1996)",
+           35
+          ],
+          [
+           980,
+           "Mother Night (1996)",
+           22
+          ],
+          [
+           981,
+           "Dangerous Ground (1997)",
+           8
+          ],
+          [
+           982,
+           "Maximum Risk (1996)",
+           20
+          ],
+          [
+           983,
+           "Rich Man's Wife, The (1996)",
+           15
+          ],
+          [
+           984,
+           "Shadow Conspiracy (1997)",
+           44
+          ],
+          [
+           985,
+           "Blood & Wine (1997)",
+           22
+          ],
+          [
+           986,
+           "Turbulence (1997)",
+           23
+          ],
+          [
+           987,
+           "Underworld (1997)",
+           4
+          ],
+          [
+           988,
+           "Beautician and the Beast, The (1997)",
+           86
+          ],
+          [
+           989,
+           "Cats Don't Dance (1997)",
+           32
+          ],
+          [
+           990,
+           "Anna Karenina (1997)",
+           33
+          ],
+          [
+           991,
+           "Keys to Tulsa (1997)",
+           25
+          ],
+          [
+           992,
+           "Head Above Water (1996)",
+           4
+          ],
+          [
+           993,
+           "Hercules (1997)",
+           66
+          ],
+          [
+           994,
+           "Last Time I Committed Suicide, The (1997)",
+           7
+          ],
+          [
+           995,
+           "Kiss Me, Guido (1997)",
+           31
+          ],
+          [
+           996,
+           "Big Green, The (1995)",
+           14
+          ],
+          [
+           997,
+           "Stuart Saves His Family (1995)",
+           16
+          ],
+          [
+           998,
+           "Cabin Boy (1994)",
+           16
+          ],
+          [
+           999,
+           "Clean Slate (1994)",
+           10
+          ],
+          [
+           1000,
+           "Lightning Jack (1994)",
+           10
+          ],
+          [
+           1001,
+           "Stupids, The (1996)",
+           17
+          ],
+          [
+           1002,
+           "Pest, The (1997)",
+           8
+          ],
+          [
+           1003,
+           "That Darn Cat! (1997)",
+           8
+          ],
+          [
+           1004,
+           "Geronimo: An American Legend (1993)",
+           9
+          ],
+          [
+           1005,
+           "Double vie de Véronique, La (Double Life of Veronique, The) (1991)",
+           22
+          ],
+          [
+           1006,
+           "Until the End of the World (Bis ans Ende der Welt) (1991)",
+           23
+          ],
+          [
+           1007,
+           "Waiting for Guffman (1996)",
+           47
+          ],
+          [
+           1008,
+           "I Shot Andy Warhol (1996)",
+           37
+          ],
+          [
+           1009,
+           "Stealing Beauty (1996)",
+           64
+          ],
+          [
+           1010,
+           "Basquiat (1996)",
+           44
+          ],
+          [
+           1011,
+           "2 Days in the Valley (1996)",
+           93
+          ],
+          [
+           1012,
+           "Private Parts (1997)",
+           100
+          ],
+          [
+           1013,
+           "Anaconda (1997)",
+           38
+          ],
+          [
+           1014,
+           "Romy and Michele's High School Reunion (1997)",
+           98
+          ],
+          [
+           1015,
+           "Shiloh (1997)",
+           12
+          ],
+          [
+           1016,
+           "Con Air (1997)",
+           137
+          ],
+          [
+           1017,
+           "Trees Lounge (1996)",
+           50
+          ],
+          [
+           1018,
+           "Tie Me Up! Tie Me Down! (1990)",
+           32
+          ],
+          [
+           1019,
+           "Die xue shuang xiong (Killer, The) (1989)",
+           31
+          ],
+          [
+           1020,
+           "Gaslight (1944)",
+           35
+          ],
+          [
+           1021,
+           "8 1/2 (1963)",
+           38
+          ],
+          [
+           1022,
+           "Fast, Cheap & Out of Control (1997)",
+           32
+          ],
+          [
+           1023,
+           "Fathers' Day (1997)",
+           31
+          ],
+          [
+           1024,
+           "Mrs. Dalloway (1997)",
+           15
+          ],
+          [
+           1025,
+           "Fire Down Below (1997)",
+           44
+          ],
+          [
+           1026,
+           "Lay of the Land, The (1997)",
+           4
+          ],
+          [
+           1027,
+           "Shooter, The (1995)",
+           3
+          ],
+          [
+           1028,
+           "Grumpier Old Men (1995)",
+           148
+          ],
+          [
+           1029,
+           "Jury Duty (1995)",
+           14
+          ],
+          [
+           1030,
+           "Beverly Hillbillies, The (1993)",
+           20
+          ],
+          [
+           1031,
+           "Lassie (1994)",
+           7
+          ],
+          [
+           1032,
+           "Little Big League (1994)",
+           16
+          ],
+          [
+           1033,
+           "Homeward Bound II: Lost in San Francisco (1996)",
+           32
+          ],
+          [
+           1034,
+           "Quest, The (1996)",
+           27
+          ],
+          [
+           1035,
+           "Cool Runnings (1993)",
+           68
+          ],
+          [
+           1036,
+           "Drop Dead Fred (1991)",
+           24
+          ],
+          [
+           1037,
+           "Grease 2 (1982)",
+           24
+          ],
+          [
+           1038,
+           "Switchback (1997)",
+           17
+          ],
+          [
+           1039,
+           "Hamlet (1996)",
+           90
+          ],
+          [
+           1040,
+           "Two if by Sea (1996)",
+           25
+          ],
+          [
+           1041,
+           "Forget Paris (1995)",
+           62
+          ],
+          [
+           1042,
+           "Just Cause (1995)",
+           28
+          ],
+          [
+           1043,
+           "Rent-a-Kid (1995)",
+           8
+          ],
+          [
+           1044,
+           "Paper, The (1994)",
+           40
+          ],
+          [
+           1045,
+           "Fearless (1993)",
+           25
+          ],
+          [
+           1046,
+           "Malice (1993)",
+           46
+          ],
+          [
+           1047,
+           "Multiplicity (1996)",
+           134
+          ],
+          [
+           1048,
+           "She's the One (1996)",
+           73
+          ],
+          [
+           1049,
+           "House Arrest (1996)",
+           25
+          ],
+          [
+           1050,
+           "Ghost and Mrs. Muir, The (1947)",
+           43
+          ],
+          [
+           1051,
+           "Associate, The (1996)",
+           41
+          ],
+          [
+           1052,
+           "Dracula: Dead and Loving It (1995)",
+           25
+          ],
+          [
+           1053,
+           "Now and Then (1995)",
+           24
+          ],
+          [
+           1054,
+           "Mr. Wrong (1996)",
+           23
+          ],
+          [
+           1055,
+           "Simple Twist of Fate, A (1994)",
+           10
+          ],
+          [
+           1056,
+           "Cronos (1992)",
+           10
+          ],
+          [
+           1057,
+           "Pallbearer, The (1996)",
+           22
+          ],
+          [
+           1058,
+           "War, The (1994)",
+           15
+          ],
+          [
+           1059,
+           "Don't Be a Menace to South Central While Drinking Your Juice in the Hood (1996)",
+           35
+          ],
+          [
+           1060,
+           "Adventures of Pinocchio, The (1996)",
+           39
+          ],
+          [
+           1061,
+           "Evening Star, The (1996)",
+           29
+          ],
+          [
+           1062,
+           "Four Days in September (1997)",
+           12
+          ],
+          [
+           1063,
+           "Little Princess, A (1995)",
+           41
+          ],
+          [
+           1064,
+           "Crossfire (1947)",
+           4
+          ],
+          [
+           1065,
+           "Koyaanisqatsi (1983)",
+           53
+          ],
+          [
+           1066,
+           "Balto (1995)",
+           16
+          ],
+          [
+           1067,
+           "Bottle Rocket (1996)",
+           44
+          ],
+          [
+           1068,
+           "Star Maker, The (Uomo delle stelle, L') (1995)",
+           12
+          ],
+          [
+           1069,
+           "Amateur (1994)",
+           18
+          ],
+          [
+           1070,
+           "Living in Oblivion (1995)",
+           27
+          ],
+          [
+           1071,
+           "Party Girl (1995)",
+           16
+          ],
+          [
+           1072,
+           "Pyromaniac's Love Story, A (1995)",
+           7
+          ],
+          [
+           1073,
+           "Shallow Grave (1994)",
+           66
+          ],
+          [
+           1074,
+           "Reality Bites (1994)",
+           77
+          ],
+          [
+           1075,
+           "Man of No Importance, A (1994)",
+           7
+          ],
+          [
+           1076,
+           "Pagemaster, The (1994)",
+           12
+          ],
+          [
+           1077,
+           "Love and a .45 (1994)",
+           8
+          ],
+          [
+           1078,
+           "Oliver & Company (1988)",
+           22
+          ],
+          [
+           1079,
+           "Joe's Apartment (1996)",
+           45
+          ],
+          [
+           1080,
+           "Celestial Clockwork (1994)",
+           2
+          ],
+          [
+           1081,
+           "Curdled (1996)",
+           8
+          ],
+          [
+           1082,
+           "Female Perversions (1996)",
+           8
+          ],
+          [
+           1083,
+           "Albino Alligator (1996)",
+           6
+          ],
+          [
+           1084,
+           "Anne Frank Remembered (1995)",
+           21
+          ],
+          [
+           1085,
+           "Carried Away (1996)",
+           11
+          ],
+          [
+           1086,
+           "It's My Party (1995)",
+           21
+          ],
+          [
+           1087,
+           "Bloodsport 2 (1995)",
+           10
+          ],
+          [
+           1088,
+           "Double Team (1997)",
+           13
+          ],
+          [
+           1089,
+           "Speed 2: Cruise Control (1997)",
+           38
+          ],
+          [
+           1090,
+           "Sliver (1993)",
+           37
+          ],
+          [
+           1091,
+           "Pete's Dragon (1977)",
+           43
+          ],
+          [
+           1092,
+           "Dear God (1996)",
+           12
+          ],
+          [
+           1093,
+           "Live Nude Girls (1995)",
+           23
+          ],
+          [
+           1094,
+           "Thin Line Between Love and Hate, A (1996)",
+           12
+          ],
+          [
+           1095,
+           "High School High (1996)",
+           29
+          ],
+          [
+           1096,
+           "Commandments (1997)",
+           3
+          ],
+          [
+           1097,
+           "Hate (Haine, La) (1995)",
+           18
+          ],
+          [
+           1098,
+           "Flirting With Disaster (1996)",
+           42
+          ],
+          [
+           1099,
+           "Red Firecracker, Green Firecracker (1994)",
+           13
+          ],
+          [
+           1100,
+           "What Happened Was... (1994)",
+           8
+          ],
+          [
+           1101,
+           "Six Degrees of Separation (1993)",
+           74
+          ],
+          [
+           1102,
+           "Two Much (1996)",
+           7
+          ],
+          [
+           1103,
+           "Trust (1990)",
+           19
+          ],
+          [
+           1104,
+           "C'est arrivé près de chez vous (1992)",
+           4
+          ],
+          [
+           1105,
+           "Firestorm (1998)",
+           18
+          ],
+          [
+           1106,
+           "Newton Boys, The (1998)",
+           4
+          ],
+          [
+           1107,
+           "Beyond Rangoon (1995)",
+           18
+          ],
+          [
+           1108,
+           "Feast of July (1995)",
+           5
+          ],
+          [
+           1109,
+           "Death and the Maiden (1994)",
+           28
+          ],
+          [
+           1110,
+           "Tank Girl (1995)",
+           41
+          ],
+          [
+           1111,
+           "Double Happiness (1994)",
+           7
+          ],
+          [
+           1112,
+           "Cobb (1994)",
+           15
+          ],
+          [
+           1113,
+           "Mrs. Parker and the Vicious Circle (1994)",
+           22
+          ],
+          [
+           1114,
+           "Faithful (1996)",
+           10
+          ],
+          [
+           1115,
+           "Twelfth Night (1996)",
+           29
+          ],
+          [
+           1116,
+           "Mark of Zorro, The (1940)",
+           13
+          ],
+          [
+           1117,
+           "Surviving Picasso (1996)",
+           19
+          ],
+          [
+           1118,
+           "Up in Smoke (1978)",
+           47
+          ],
+          [
+           1119,
+           "Some Kind of Wonderful (1987)",
+           59
+          ],
+          [
+           1120,
+           "I'm Not Rappaport (1996)",
+           17
+          ],
+          [
+           1121,
+           "Umbrellas of Cherbourg, The (Parapluies de Cherbourg, Les) (1964)",
+           21
+          ],
+          [
+           1122,
+           "They Made Me a Criminal (1939)",
+           1
+          ],
+          [
+           1123,
+           "Last Time I Saw Paris, The (1954)",
+           3
+          ],
+          [
+           1124,
+           "Farewell to Arms, A (1932)",
+           12
+          ],
+          [
+           1125,
+           "Innocents, The (1961)",
+           4
+          ],
+          [
+           1126,
+           "Old Man and the Sea, The (1958)",
+           32
+          ],
+          [
+           1127,
+           "Truman Show, The (1998)",
+           11
+          ],
+          [
+           1128,
+           "Heidi Fleiss: Hollywood Madam (1995) ",
+           13
+          ],
+          [
+           1129,
+           "Chungking Express (1994)",
+           28
+          ],
+          [
+           1130,
+           "Jupiter's Wife (1994)",
+           1
+          ],
+          [
+           1131,
+           "Safe (1995)",
+           13
+          ],
+          [
+           1132,
+           "Feeling Minnesota (1996)",
+           32
+          ],
+          [
+           1133,
+           "Escape to Witch Mountain (1975)",
+           30
+          ],
+          [
+           1134,
+           "Get on the Bus (1996)",
+           38
+          ],
+          [
+           1135,
+           "Doors, The (1991)",
+           46
+          ],
+          [
+           1136,
+           "Ghosts of Mississippi (1996)",
+           29
+          ],
+          [
+           1137,
+           "Beautiful Thing (1996)",
+           29
+          ],
+          [
+           1138,
+           "Best Men (1997)",
+           5
+          ],
+          [
+           1139,
+           "Hackers (1995)",
+           33
+          ],
+          [
+           1140,
+           "Road to Wellville, The (1994)",
+           17
+          ],
+          [
+           1141,
+           "War Room, The (1993)",
+           9
+          ],
+          [
+           1142,
+           "When We Were Kings (1996)",
+           44
+          ],
+          [
+           1143,
+           "Hard Eight (1996)",
+           15
+          ],
+          [
+           1144,
+           "Quiet Room, The (1996)",
+           3
+          ],
+          [
+           1145,
+           "Blue Chips (1994)",
+           9
+          ],
+          [
+           1146,
+           "Calendar Girl (1993)",
+           3
+          ],
+          [
+           1147,
+           "My Family (1995)",
+           21
+          ],
+          [
+           1148,
+           "Tom & Viv (1994)",
+           9
+          ],
+          [
+           1149,
+           "Walkabout (1971)",
+           26
+          ],
+          [
+           1150,
+           "Last Dance (1996)",
+           9
+          ],
+          [
+           1151,
+           "Original Gangstas (1996)",
+           7
+          ],
+          [
+           1152,
+           "In Love and War (1996)",
+           28
+          ],
+          [
+           1153,
+           "Backbeat (1993)",
+           19
+          ],
+          [
+           1154,
+           "Alphaville (1965)",
+           12
+          ],
+          [
+           1155,
+           "Rendezvous in Paris (Rendez-vous de Paris, Les) (1995)",
+           3
+          ],
+          [
+           1156,
+           "Cyclo (1995)",
+           1
+          ],
+          [
+           1157,
+           "Relic, The (1997)",
+           25
+          ],
+          [
+           1158,
+           "Fille seule, La (A Single Girl) (1995)",
+           4
+          ],
+          [
+           1159,
+           "Stalker (1979)",
+           11
+          ],
+          [
+           1160,
+           "Love! Valour! Compassion! (1997)",
+           26
+          ],
+          [
+           1161,
+           "Palookaville (1996)",
+           13
+          ],
+          [
+           1162,
+           "Phat Beach (1996)",
+           5
+          ],
+          [
+           1163,
+           "Portrait of a Lady, The (1996)",
+           25
+          ],
+          [
+           1164,
+           "Zeus and Roxanne (1997)",
+           6
+          ],
+          [
+           1165,
+           "Big Bully (1996)",
+           14
+          ],
+          [
+           1166,
+           "Love & Human Remains (1993)",
+           12
+          ],
+          [
+           1167,
+           "Sum of Us, The (1994)",
+           11
+          ],
+          [
+           1168,
+           "Little Buddha (1993)",
+           22
+          ],
+          [
+           1169,
+           "Fresh (1994)",
+           10
+          ],
+          [
+           1170,
+           "Spanking the Monkey (1994)",
+           27
+          ],
+          [
+           1171,
+           "Wild Reeds (1994)",
+           14
+          ],
+          [
+           1172,
+           "Women, The (1939)",
+           15
+          ],
+          [
+           1173,
+           "Bliss (1997)",
+           7
+          ],
+          [
+           1174,
+           "Caught (1996)",
+           8
+          ],
+          [
+           1175,
+           "Hugo Pool (1997)",
+           5
+          ],
+          [
+           1176,
+           "Welcome To Sarajevo (1997)",
+           22
+          ],
+          [
+           1177,
+           "Dunston Checks In (1996)",
+           7
+          ],
+          [
+           1178,
+           "Major Payne (1994)",
+           19
+          ],
+          [
+           1179,
+           "Man of the House (1995)",
+           9
+          ],
+          [
+           1180,
+           "I Love Trouble (1994)",
+           10
+          ],
+          [
+           1181,
+           "Low Down Dirty Shame, A (1994)",
+           10
+          ],
+          [
+           1182,
+           "Cops and Robbersons (1994)",
+           13
+          ],
+          [
+           1183,
+           "Cowboy Way, The (1994)",
+           19
+          ],
+          [
+           1184,
+           "Endless Summer 2, The (1994)",
+           10
+          ],
+          [
+           1185,
+           "In the Army Now (1994)",
+           18
+          ],
+          [
+           1186,
+           "Inkwell, The (1994)",
+           3
+          ],
+          [
+           1187,
+           "Switchblade Sisters (1975)",
+           13
+          ],
+          [
+           1188,
+           "Young Guns II (1990)",
+           44
+          ],
+          [
+           1189,
+           "Prefontaine (1997)",
+           3
+          ],
+          [
+           1190,
+           "That Old Feeling (1997)",
+           11
+          ],
+          [
+           1191,
+           "Letter From Death Row, A (1998)",
+           3
+          ],
+          [
+           1192,
+           "Boys of St. Vincent, The (1993)",
+           13
+          ],
+          [
+           1193,
+           "Before the Rain (Pred dozhdot) (1994)",
+           10
+          ],
+          [
+           1194,
+           "Once Were Warriors (1994)",
+           31
+          ],
+          [
+           1195,
+           "Strawberry and Chocolate (Fresa y chocolate) (1993)",
+           11
+          ],
+          [
+           1196,
+           "Savage Nights (Nuits fauves, Les) (1992)",
+           3
+          ],
+          [
+           1197,
+           "Family Thing, A (1996)",
+           45
+          ],
+          [
+           1198,
+           "Purple Noon (1960)",
+           7
+          ],
+          [
+           1199,
+           "Cemetery Man (Dellamorte Dellamore) (1994)",
+           23
+          ],
+          [
+           1200,
+           "Kim (1950)",
+           7
+          ],
+          [
+           1201,
+           "Marlene Dietrich: Shadow and Light (1996) ",
+           1
+          ],
+          [
+           1202,
+           "Maybe, Maybe Not (Bewegte Mann, Der) (1994)",
+           8
+          ],
+          [
+           1203,
+           "Top Hat (1935)",
+           21
+          ],
+          [
+           1204,
+           "To Be or Not to Be (1942)",
+           18
+          ],
+          [
+           1205,
+           "Secret Agent, The (1996)",
+           6
+          ],
+          [
+           1206,
+           "Amos & Andrew (1993)",
+           19
+          ],
+          [
+           1207,
+           "Jade (1995)",
+           17
+          ],
+          [
+           1208,
+           "Kiss of Death (1995)",
+           20
+          ],
+          [
+           1209,
+           "Mixed Nuts (1994)",
+           15
+          ],
+          [
+           1210,
+           "Virtuosity (1995)",
+           38
+          ],
+          [
+           1211,
+           "Blue Sky (1994)",
+           12
+          ],
+          [
+           1212,
+           "Flesh and Bone (1993)",
+           6
+          ],
+          [
+           1213,
+           "Guilty as Sin (1993)",
+           6
+          ],
+          [
+           1214,
+           "In the Realm of the Senses (Ai no corrida) (1976)",
+           9
+          ],
+          [
+           1215,
+           "Barb Wire (1996)",
+           30
+          ],
+          [
+           1216,
+           "Kissed (1996)",
+           6
+          ],
+          [
+           1217,
+           "Assassins (1995)",
+           39
+          ],
+          [
+           1218,
+           "Friday (1995)",
+           26
+          ],
+          [
+           1219,
+           "Goofy Movie, A (1995)",
+           20
+          ],
+          [
+           1220,
+           "Higher Learning (1995)",
+           30
+          ],
+          [
+           1221,
+           "When a Man Loves a Woman (1994)",
+           39
+          ],
+          [
+           1222,
+           "Judgment Night (1993)",
+           25
+          ],
+          [
+           1223,
+           "King of the Hill (1993)",
+           4
+          ],
+          [
+           1224,
+           "Scout, The (1994)",
+           12
+          ],
+          [
+           1225,
+           "Angus (1995)",
+           14
+          ],
+          [
+           1226,
+           "Night Falls on Manhattan (1997)",
+           32
+          ],
+          [
+           1227,
+           "Awfully Big Adventure, An (1995)",
+           8
+          ],
+          [
+           1228,
+           "Under Siege 2: Dark Territory (1995)",
+           48
+          ],
+          [
+           1229,
+           "Poison Ivy II (1995)",
+           13
+          ],
+          [
+           1230,
+           "Ready to Wear (Pret-A-Porter) (1994)",
+           18
+          ],
+          [
+           1231,
+           "Marked for Death (1990)",
+           22
+          ],
+          [
+           1232,
+           "Madonna: Truth or Dare (1991)",
+           17
+          ],
+          [
+           1233,
+           "Nénette et Boni (1996)",
+           6
+          ],
+          [
+           1234,
+           "Chairman of the Board (1998)",
+           8
+          ],
+          [
+           1235,
+           "Big Bang Theory, The (1994)",
+           1
+          ],
+          [
+           1236,
+           "Other Voices, Other Rooms (1997)",
+           1
+          ],
+          [
+           1237,
+           "Twisted (1996)",
+           6
+          ],
+          [
+           1238,
+           "Full Speed (1996)",
+           8
+          ],
+          [
+           1239,
+           "Cutthroat Island (1995)",
+           18
+          ],
+          [
+           1240,
+           "Ghost in the Shell (Kokaku kidotai) (1995)",
+           26
+          ],
+          [
+           1241,
+           "Van, The (1996)",
+           6
+          ],
+          [
+           1242,
+           "Old Lady Who Walked in the Sea, The (Vieille qui marchait dans la mer, La) (1991)",
+           5
+          ],
+          [
+           1243,
+           "Night Flier (1997)",
+           7
+          ],
+          [
+           1244,
+           "Metro (1997)",
+           36
+          ],
+          [
+           1245,
+           "Gridlock'd (1997)",
+           19
+          ],
+          [
+           1246,
+           "Bushwhacked (1995)",
+           7
+          ],
+          [
+           1247,
+           "Bad Girls (1994)",
+           6
+          ],
+          [
+           1248,
+           "Blink (1994)",
+           19
+          ],
+          [
+           1249,
+           "For Love or Money (1993)",
+           12
+          ],
+          [
+           1250,
+           "Best of the Best 3: No Turning Back (1995)",
+           6
+          ],
+          [
+           1251,
+           "A Chef in Love (1996)",
+           8
+          ],
+          [
+           1252,
+           "Contempt (Mépris, Le) (1963)",
+           9
+          ],
+          [
+           1253,
+           "Tie That Binds, The (1995)",
+           7
+          ],
+          [
+           1254,
+           "Gone Fishin' (1997)",
+           11
+          ],
+          [
+           1255,
+           "Broken English (1996)",
+           8
+          ],
+          [
+           1256,
+           "Designated Mourner, The (1997)",
+           3
+          ],
+          [
+           1257,
+           "Designated Mourner, The (1997)",
+           4
+          ],
+          [
+           1258,
+           "Trial and Error (1997)",
+           23
+          ],
+          [
+           1259,
+           "Pie in the Sky (1995)",
+           4
+          ],
+          [
+           1260,
+           "Total Eclipse (1995)",
+           4
+          ],
+          [
+           1261,
+           "Run of the Country, The (1995)",
+           4
+          ],
+          [
+           1262,
+           "Walking and Talking (1996)",
+           8
+          ],
+          [
+           1263,
+           "Foxfire (1996)",
+           15
+          ],
+          [
+           1264,
+           "Nothing to Lose (1994)",
+           7
+          ],
+          [
+           1265,
+           "Star Maps (1997)",
+           19
+          ],
+          [
+           1266,
+           "Bread and Chocolate (Pane e cioccolata) (1973)",
+           12
+          ],
+          [
+           1267,
+           "Clockers (1995)",
+           33
+          ],
+          [
+           1268,
+           "Bitter Moon (1992)",
+           10
+          ],
+          [
+           1269,
+           "Love in the Afternoon (1957)",
+           10
+          ],
+          [
+           1270,
+           "Life with Mikey (1993)",
+           7
+          ],
+          [
+           1271,
+           "North (1994)",
+           7
+          ],
+          [
+           1272,
+           "Talking About Sex (1994)",
+           5
+          ],
+          [
+           1273,
+           "Color of Night (1994)",
+           15
+          ],
+          [
+           1274,
+           "Robocop 3 (1993)",
+           11
+          ],
+          [
+           1275,
+           "Killer (Bulletproof Heart) (1994)",
+           4
+          ],
+          [
+           1276,
+           "Sunset Park (1996)",
+           8
+          ],
+          [
+           1277,
+           "Set It Off (1996)",
+           19
+          ],
+          [
+           1278,
+           "Selena (1997)",
+           16
+          ],
+          [
+           1279,
+           "Wild America (1997)",
+           9
+          ],
+          [
+           1280,
+           "Gang Related (1997)",
+           16
+          ],
+          [
+           1281,
+           "Manny & Lo (1996)",
+           13
+          ],
+          [
+           1282,
+           "Grass Harp, The (1995)",
+           9
+          ],
+          [
+           1283,
+           "Out to Sea (1997)",
+           19
+          ],
+          [
+           1284,
+           "Before and After (1996)",
+           26
+          ],
+          [
+           1285,
+           "Princess Caraboo (1994)",
+           15
+          ],
+          [
+           1286,
+           "Shall We Dance? (1937)",
+           17
+          ],
+          [
+           1287,
+           "Ed (1996)",
+           6
+          ],
+          [
+           1288,
+           "Denise Calls Up (1995)",
+           7
+          ],
+          [
+           1289,
+           "Jack and Sarah (1995)",
+           7
+          ],
+          [
+           1290,
+           "Country Life (1994)",
+           2
+          ],
+          [
+           1291,
+           "Celtic Pride (1996)",
+           15
+          ],
+          [
+           1292,
+           "Simple Wish, A (1997)",
+           3
+          ],
+          [
+           1293,
+           "Star Kid (1997)",
+           3
+          ],
+          [
+           1294,
+           "Ayn Rand: A Sense of Life (1997)",
+           7
+          ],
+          [
+           1295,
+           "Kicked in the Head (1997)",
+           7
+          ],
+          [
+           1296,
+           "Indian Summer (1996)",
+           20
+          ],
+          [
+           1297,
+           "Love Affair (1994)",
+           12
+          ],
+          [
+           1298,
+           "Band Wagon, The (1953)",
+           9
+          ],
+          [
+           1299,
+           "Penny Serenade (1941)",
+           8
+          ],
+          [
+           1300,
+           "'Til There Was You (1997)",
+           9
+          ],
+          [
+           1301,
+           "Stripes (1981)",
+           5
+          ],
+          [
+           1302,
+           "Late Bloomers (1996)",
+           5
+          ],
+          [
+           1303,
+           "Getaway, The (1994)",
+           18
+          ],
+          [
+           1304,
+           "New York Cop (1996)",
+           2
+          ],
+          [
+           1305,
+           "National Lampoon's Senior Trip (1995)",
+           7
+          ],
+          [
+           1306,
+           "Delta of Venus (1994)",
+           2
+          ],
+          [
+           1307,
+           "Carmen Miranda: Bananas Is My Business (1994)",
+           2
+          ],
+          [
+           1308,
+           "Babyfever (1994)",
+           2
+          ],
+          [
+           1309,
+           "Very Natural Thing, A (1974)",
+           1
+          ],
+          [
+           1310,
+           "Walk in the Sun, A (1945)",
+           1
+          ],
+          [
+           1311,
+           "Waiting to Exhale (1995)",
+           16
+          ],
+          [
+           1312,
+           "Pompatus of Love, The (1996)",
+           7
+          ],
+          [
+           1313,
+           "Palmetto (1998)",
+           14
+          ],
+          [
+           1314,
+           "Surviving the Game (1994)",
+           11
+          ],
+          [
+           1315,
+           "Inventing the Abbotts (1997)",
+           23
+          ],
+          [
+           1316,
+           "Horse Whisperer, The (1998)",
+           7
+          ],
+          [
+           1317,
+           "Journey of August King, The (1995)",
+           4
+          ],
+          [
+           1318,
+           "Catwalk (1995)",
+           3
+          ],
+          [
+           1319,
+           "Neon Bible, The (1995)",
+           4
+          ],
+          [
+           1320,
+           "Homage (1995)",
+           1
+          ],
+          [
+           1321,
+           "Open Season (1996)",
+           2
+          ],
+          [
+           1322,
+           "Metisse (Café au Lait) (1993)",
+           6
+          ],
+          [
+           1323,
+           "Wooden Man's Bride, The (Wu Kui) (1994)",
+           3
+          ],
+          [
+           1324,
+           "Loaded (1994)",
+           5
+          ],
+          [
+           1325,
+           "August (1996)",
+           1
+          ],
+          [
+           1326,
+           "Boys (1996)",
+           6
+          ],
+          [
+           1327,
+           "Captives (1994)",
+           3
+          ],
+          [
+           1328,
+           "Of Love and Shadows (1994)",
+           6
+          ],
+          [
+           1329,
+           "Low Life, The (1994)",
+           1
+          ],
+          [
+           1330,
+           "An Unforgettable Summer (1994)",
+           4
+          ],
+          [
+           1331,
+           "Last Klezmer: Leopold Kozlowski, His Life and Music, The (1995)",
+           4
+          ],
+          [
+           1332,
+           "My Life and Times With Antonin Artaud (En compagnie d'Antonin Artaud) (1993)",
+           2
+          ],
+          [
+           1333,
+           "Midnight Dancers (Sibak) (1994)",
+           5
+          ],
+          [
+           1334,
+           "Somebody to Love (1994)",
+           2
+          ],
+          [
+           1335,
+           "American Buffalo (1996)",
+           11
+          ],
+          [
+           1336,
+           "Kazaam (1996)",
+           10
+          ],
+          [
+           1337,
+           "Larger Than Life (1996)",
+           9
+          ],
+          [
+           1338,
+           "Two Deaths (1995)",
+           4
+          ],
+          [
+           1339,
+           "Stefano Quantestorie (1993)",
+           1
+          ],
+          [
+           1340,
+           "Crude Oasis, The (1995)",
+           1
+          ],
+          [
+           1341,
+           "Hedd Wyn (1992)",
+           1
+          ],
+          [
+           1342,
+           "Convent, The (Convento, O) (1995)",
+           2
+          ],
+          [
+           1343,
+           "Lotto Land (1995)",
+           1
+          ],
+          [
+           1344,
+           "Story of Xinghua, The (1993)",
+           5
+          ],
+          [
+           1345,
+           "Day the Sun Turned Cold, The (Tianguo niezi) (1994)",
+           2
+          ],
+          [
+           1346,
+           "Dingo (1992)",
+           5
+          ],
+          [
+           1347,
+           "Ballad of Narayama, The (Narayama Bushiko) (1958)",
+           4
+          ],
+          [
+           1348,
+           "Every Other Weekend (1990)",
+           1
+          ],
+          [
+           1349,
+           "Mille bolle blu (1993)",
+           1
+          ],
+          [
+           1350,
+           "Crows and Sparrows (1949)",
+           2
+          ],
+          [
+           1351,
+           "Lover's Knot (1996)",
+           3
+          ],
+          [
+           1352,
+           "Shadow of Angels (Schatten der Engel) (1976)",
+           1
+          ],
+          [
+           1353,
+           "1-900 (1994)",
+           5
+          ],
+          [
+           1354,
+           "Venice/Venice (1992)",
+           2
+          ],
+          [
+           1355,
+           "Infinity (1996)",
+           6
+          ],
+          [
+           1356,
+           "Ed's Next Move (1996)",
+           3
+          ],
+          [
+           1357,
+           "For the Moment (1994)",
+           3
+          ],
+          [
+           1358,
+           "The Deadly Cure (1996)",
+           2
+          ],
+          [
+           1359,
+           "Boys in Venice (1996)",
+           2
+          ],
+          [
+           1360,
+           "Sexual Life of the Belgians, The (1994)",
+           2
+          ],
+          [
+           1361,
+           "Search for One-eye Jimmy, The (1996)",
+           3
+          ],
+          [
+           1362,
+           "American Strays (1996)",
+           2
+          ],
+          [
+           1363,
+           "Leopard Son, The (1996)",
+           1
+          ],
+          [
+           1364,
+           "Bird of Prey (1996)",
+           1
+          ],
+          [
+           1365,
+           "Johnny 100 Pesos (1993)",
+           2
+          ],
+          [
+           1366,
+           "JLG/JLG - autoportrait de décembre (1994)",
+           1
+          ],
+          [
+           1367,
+           "Faust (1994)",
+           5
+          ],
+          [
+           1368,
+           "Mina Tannenbaum (1994)",
+           6
+          ],
+          [
+           1369,
+           "Forbidden Christ, The (Cristo proibito, Il) (1950)",
+           4
+          ],
+          [
+           1370,
+           "I Can't Sleep (J'ai pas sommeil) (1994)",
+           3
+          ],
+          [
+           1371,
+           "Machine, The (1994)",
+           2
+          ],
+          [
+           1372,
+           "Stranger, The (1994)",
+           3
+          ],
+          [
+           1373,
+           "Good Morning (1971)",
+           1
+          ],
+          [
+           1374,
+           "Falling in Love Again (1980)",
+           2
+          ],
+          [
+           1375,
+           "Cement Garden, The (1993)",
+           10
+          ],
+          [
+           1376,
+           "Meet Wally Sparks (1997)",
+           7
+          ],
+          [
+           1377,
+           "Hotel de Love (1996)",
+           4
+          ],
+          [
+           1378,
+           "Rhyme & Reason (1997)",
+           5
+          ],
+          [
+           1379,
+           "Love and Other Catastrophes (1996)",
+           7
+          ],
+          [
+           1380,
+           "Hollow Reed (1996)",
+           6
+          ],
+          [
+           1381,
+           "Losing Chase (1996)",
+           8
+          ],
+          [
+           1382,
+           "Bonheur, Le (1965)",
+           4
+          ],
+          [
+           1383,
+           "Second Jungle Book: Mowgli & Baloo, The (1997)",
+           6
+          ],
+          [
+           1384,
+           "Squeeze (1996)",
+           3
+          ],
+          [
+           1385,
+           "Roseanna's Grave (For Roseanna) (1997)",
+           5
+          ],
+          [
+           1386,
+           "Tetsuo II: Body Hammer (1992)",
+           6
+          ],
+          [
+           1387,
+           "Fall (1997)",
+           3
+          ],
+          [
+           1388,
+           "Gabbeh (1996)",
+           6
+          ],
+          [
+           1389,
+           "Mondo (1996)",
+           3
+          ],
+          [
+           1390,
+           "Innocent Sleep, The (1995)",
+           2
+          ],
+          [
+           1391,
+           "For Ever Mozart (1996)",
+           3
+          ],
+          [
+           1392,
+           "Locusts, The (1997)",
+           5
+          ],
+          [
+           1393,
+           "Stag (1997)",
+           9
+          ],
+          [
+           1394,
+           "Swept from the Sea (1997)",
+           7
+          ],
+          [
+           1395,
+           "Hurricane Streets (1998)",
+           6
+          ],
+          [
+           1396,
+           "Stonewall (1995)",
+           5
+          ],
+          [
+           1397,
+           "Of Human Bondage (1934)",
+           5
+          ],
+          [
+           1398,
+           "Anna (1996)",
+           2
+          ],
+          [
+           1399,
+           "Stranger in the House (1997)",
+           7
+          ],
+          [
+           1400,
+           "Picture Bride (1995)",
+           10
+          ],
+          [
+           1401,
+           "M. Butterfly (1993)",
+           18
+          ],
+          [
+           1402,
+           "Ciao, Professore! (1993)",
+           4
+          ],
+          [
+           1403,
+           "Caro Diario (Dear Diary) (1994)",
+           4
+          ],
+          [
+           1404,
+           "Withnail and I (1987)",
+           13
+          ],
+          [
+           1405,
+           "Boy's Life 2 (1997)",
+           6
+          ],
+          [
+           1406,
+           "When Night Is Falling (1995)",
+           5
+          ],
+          [
+           1407,
+           "Specialist, The (1994)",
+           20
+          ],
+          [
+           1408,
+           "Gordy (1995)",
+           3
+          ],
+          [
+           1409,
+           "Swan Princess, The (1994)",
+           7
+          ],
+          [
+           1410,
+           "Harlem (1993)",
+           4
+          ],
+          [
+           1411,
+           "Barbarella (1968)",
+           28
+          ],
+          [
+           1412,
+           "Land Before Time III: The Time of the Great Giving (1995) (V)",
+           6
+          ],
+          [
+           1413,
+           "Street Fighter (1994)",
+           8
+          ],
+          [
+           1414,
+           "Coldblooded (1995)",
+           1
+          ],
+          [
+           1415,
+           "Next Karate Kid, The (1994)",
+           9
+          ],
+          [
+           1416,
+           "No Escape (1994)",
+           5
+          ],
+          [
+           1417,
+           "Turning, The (1992)",
+           2
+          ],
+          [
+           1418,
+           "Joy Luck Club, The (1993)",
+           3
+          ],
+          [
+           1419,
+           "Highlander III: The Sorcerer (1994)",
+           16
+          ],
+          [
+           1420,
+           "Gilligan's Island: The Movie (1998)",
+           3
+          ],
+          [
+           1421,
+           "My Crazy Life (Mi vida loca) (1993)",
+           11
+          ],
+          [
+           1422,
+           "Suture (1993)",
+           4
+          ],
+          [
+           1423,
+           "Walking Dead, The (1995)",
+           4
+          ],
+          [
+           1424,
+           "I Like It Like That (1994)",
+           3
+          ],
+          [
+           1425,
+           "I'll Do Anything (1994)",
+           10
+          ],
+          [
+           1426,
+           "Grace of My Heart (1996)",
+           8
+          ],
+          [
+           1427,
+           "Drunks (1995)",
+           5
+          ],
+          [
+           1428,
+           "SubUrbia (1997)",
+           12
+          ],
+          [
+           1429,
+           "Sliding Doors (1998)",
+           4
+          ],
+          [
+           1430,
+           "Ill Gotten Gains (1997)",
+           3
+          ],
+          [
+           1431,
+           "Legal Deceit (1997)",
+           5
+          ],
+          [
+           1432,
+           "Mighty, The (1998)",
+           3
+          ],
+          [
+           1433,
+           "Men of Means (1998)",
+           2
+          ],
+          [
+           1434,
+           "Shooting Fish (1997)",
+           10
+          ],
+          [
+           1435,
+           "Steal Big, Steal Little (1995)",
+           7
+          ],
+          [
+           1436,
+           "Mr. Jones (1993)",
+           2
+          ],
+          [
+           1437,
+           "House Party 3 (1994)",
+           9
+          ],
+          [
+           1438,
+           "Panther (1995)",
+           5
+          ],
+          [
+           1439,
+           "Jason's Lyric (1994)",
+           8
+          ],
+          [
+           1440,
+           "Above the Rim (1994)",
+           5
+          ],
+          [
+           1441,
+           "Moonlight and Valentino (1995)",
+           7
+          ],
+          [
+           1442,
+           "Scarlet Letter, The (1995)",
+           5
+          ],
+          [
+           1443,
+           "8 Seconds (1994)",
+           4
+          ],
+          [
+           1444,
+           "That Darn Cat! (1965)",
+           19
+          ],
+          [
+           1445,
+           "Ladybird Ladybird (1994)",
+           4
+          ],
+          [
+           1446,
+           "Bye Bye, Love (1995)",
+           15
+          ],
+          [
+           1447,
+           "Century (1993)",
+           1
+          ],
+          [
+           1448,
+           "My Favorite Season (1993)",
+           3
+          ],
+          [
+           1449,
+           "Pather Panchali (1955)",
+           8
+          ],
+          [
+           1450,
+           "Golden Earrings (1947)",
+           2
+          ],
+          [
+           1451,
+           "Foreign Correspondent (1940)",
+           15
+          ],
+          [
+           1452,
+           "Lady of Burlesque (1943)",
+           1
+          ],
+          [
+           1453,
+           "Angel on My Shoulder (1946)",
+           1
+          ],
+          [
+           1454,
+           "Angel and the Badman (1947)",
+           6
+          ],
+          [
+           1455,
+           "Outlaw, The (1943)",
+           2
+          ],
+          [
+           1456,
+           "Beat the Devil (1954)",
+           7
+          ],
+          [
+           1457,
+           "Love Is All There Is (1996)",
+           1
+          ],
+          [
+           1458,
+           "Damsel in Distress, A (1937)",
+           1
+          ],
+          [
+           1459,
+           "Madame Butterfly (1995)",
+           7
+          ],
+          [
+           1460,
+           "Sleepover (1995)",
+           1
+          ],
+          [
+           1461,
+           "Here Comes Cookie (1935)",
+           1
+          ],
+          [
+           1462,
+           "Thieves (Voleurs, Les) (1996)",
+           7
+          ],
+          [
+           1463,
+           "Boys, Les (1997)",
+           3
+          ],
+          [
+           1464,
+           "Stars Fell on Henrietta, The (1995)",
+           3
+          ],
+          [
+           1465,
+           "Last Summer in the Hamptons (1995)",
+           3
+          ],
+          [
+           1466,
+           "Margaret's Museum (1995)",
+           6
+          ],
+          [
+           1467,
+           "Saint of Fort Washington, The (1993)",
+           2
+          ],
+          [
+           1468,
+           "Cure, The (1995)",
+           6
+          ],
+          [
+           1469,
+           "Tom and Huck (1995)",
+           12
+          ],
+          [
+           1470,
+           "Gumby: The Movie (1995)",
+           5
+          ],
+          [
+           1471,
+           "Hideaway (1995)",
+           9
+          ],
+          [
+           1472,
+           "Visitors, The (Visiteurs, Les) (1993)",
+           2
+          ],
+          [
+           1473,
+           "Little Princess, The (1939)",
+           9
+          ],
+          [
+           1474,
+           "Nina Takes a Lover (1994)",
+           6
+          ],
+          [
+           1475,
+           "Bhaji on the Beach (1993)",
+           8
+          ],
+          [
+           1476,
+           "Raw Deal (1948)",
+           1
+          ],
+          [
+           1477,
+           "Nightwatch (1997)",
+           2
+          ],
+          [
+           1478,
+           "Dead Presidents (1995)",
+           18
+          ],
+          [
+           1479,
+           "Reckless (1995)",
+           8
+          ],
+          [
+           1480,
+           "Herbie Rides Again (1974)",
+           11
+          ],
+          [
+           1481,
+           "S.F.W. (1994)",
+           2
+          ],
+          [
+           1482,
+           "Gate of Heavenly Peace, The (1995)",
+           1
+          ],
+          [
+           1483,
+           "Man in the Iron Mask, The (1998)",
+           12
+          ],
+          [
+           1484,
+           "Jerky Boys, The (1994)",
+           3
+          ],
+          [
+           1485,
+           "Colonel Chabert, Le (1994)",
+           4
+          ],
+          [
+           1486,
+           "Girl in the Cadillac (1995)",
+           1
+          ],
+          [
+           1487,
+           "Even Cowgirls Get the Blues (1993)",
+           5
+          ],
+          [
+           1488,
+           "Germinal (1993)",
+           4
+          ],
+          [
+           1489,
+           "Chasers (1994)",
+           5
+          ],
+          [
+           1490,
+           "Fausto (1993)",
+           3
+          ],
+          [
+           1491,
+           "Tough and Deadly (1995)",
+           2
+          ],
+          [
+           1492,
+           "Window to Paris (1994)",
+           1
+          ],
+          [
+           1493,
+           "Modern Affair, A (1995)",
+           1
+          ],
+          [
+           1494,
+           "Mostro, Il (1994)",
+           1
+          ],
+          [
+           1495,
+           "Flirt (1995)",
+           5
+          ],
+          [
+           1496,
+           "Carpool (1996)",
+           5
+          ],
+          [
+           1497,
+           "Line King: Al Hirschfeld, The (1996)",
+           2
+          ],
+          [
+           1498,
+           "Farmer & Chase (1995)",
+           1
+          ],
+          [
+           1499,
+           "Grosse Fatigue (1994)",
+           4
+          ],
+          [
+           1500,
+           "Santa with Muscles (1996)",
+           2
+          ],
+          [
+           1501,
+           "Prisoner of the Mountains (Kavkazsky Plennik) (1996)",
+           5
+          ],
+          [
+           1502,
+           "Naked in New York (1994)",
+           2
+          ],
+          [
+           1503,
+           "Gold Diggers: The Secret of Bear Mountain (1995)",
+           10
+          ],
+          [
+           1504,
+           "Bewegte Mann, Der (1994)",
+           3
+          ],
+          [
+           1505,
+           "Killer: A Journal of Murder (1995)",
+           1
+          ],
+          [
+           1506,
+           "Nelly & Monsieur Arnaud (1995)",
+           3
+          ],
+          [
+           1507,
+           "Three Lives and Only One Death (1996)",
+           1
+          ],
+          [
+           1508,
+           "Babysitter, The (1995)",
+           3
+          ],
+          [
+           1509,
+           "Getting Even with Dad (1994)",
+           5
+          ],
+          [
+           1510,
+           "Mad Dog Time (1996)",
+           1
+          ],
+          [
+           1511,
+           "Children of the Revolution (1996)",
+           5
+          ],
+          [
+           1512,
+           "World of Apu, The (Apur Sansar) (1959)",
+           6
+          ],
+          [
+           1513,
+           "Sprung (1997)",
+           3
+          ],
+          [
+           1514,
+           "Dream With the Fishes (1997)",
+           7
+          ],
+          [
+           1515,
+           "Wings of Courage (1995)",
+           1
+          ],
+          [
+           1516,
+           "Wedding Gift, The (1994)",
+           3
+          ],
+          [
+           1517,
+           "Race the Sun (1996)",
+           5
+          ],
+          [
+           1518,
+           "Losing Isaiah (1995)",
+           12
+          ],
+          [
+           1519,
+           "New Jersey Drive (1995)",
+           2
+          ],
+          [
+           1520,
+           "Fear, The (1995)",
+           1
+          ],
+          [
+           1521,
+           "Mr. Wonderful (1993)",
+           4
+          ],
+          [
+           1522,
+           "Trial by Jury (1994)",
+           7
+          ],
+          [
+           1523,
+           "Good Man in Africa, A (1994)",
+           2
+          ],
+          [
+           1524,
+           "Kaspar Hauser (1993)",
+           8
+          ],
+          [
+           1525,
+           "Object of My Affection, The (1998)",
+           1
+          ],
+          [
+           1526,
+           "Witness (1985)",
+           1
+          ],
+          [
+           1527,
+           "Senseless (1998)",
+           7
+          ],
+          [
+           1528,
+           "Nowhere (1997)",
+           3
+          ],
+          [
+           1529,
+           "Underground (1995)",
+           5
+          ],
+          [
+           1530,
+           "Jefferson in Paris (1995)",
+           5
+          ],
+          [
+           1531,
+           "Far From Home: The Adventures of Yellow Dog (1995)",
+           7
+          ],
+          [
+           1532,
+           "Foreign Student (1994)",
+           2
+          ],
+          [
+           1533,
+           "I Don't Want to Talk About It (De eso no se habla) (1993)",
+           1
+          ],
+          [
+           1534,
+           "Twin Town (1997)",
+           6
+          ],
+          [
+           1535,
+           "Enfer, L' (1994)",
+           4
+          ],
+          [
+           1536,
+           "Aiqing wansui (1994)",
+           1
+          ],
+          [
+           1537,
+           "Cosi (1996)",
+           4
+          ],
+          [
+           1538,
+           "All Over Me (1997)",
+           3
+          ],
+          [
+           1539,
+           "Being Human (1993)",
+           4
+          ],
+          [
+           1540,
+           "Amazing Panda Adventure, The (1995)",
+           10
+          ],
+          [
+           1541,
+           "Beans of Egypt, Maine, The (1994)",
+           2
+          ],
+          [
+           1542,
+           "Scarlet Letter, The (1926)",
+           2
+          ],
+          [
+           1543,
+           "Johns (1996)",
+           1
+          ],
+          [
+           1544,
+           "It Takes Two (1995)",
+           3
+          ],
+          [
+           1545,
+           "Frankie Starlight (1995)",
+           4
+          ],
+          [
+           1546,
+           "Shadows (Cienie) (1988)",
+           1
+          ],
+          [
+           1547,
+           "Show, The (1995)",
+           2
+          ],
+          [
+           1548,
+           "The Courtyard (1995)",
+           1
+          ],
+          [
+           1549,
+           "Dream Man (1995)",
+           2
+          ],
+          [
+           1550,
+           "Destiny Turns on the Radio (1995)",
+           2
+          ],
+          [
+           1551,
+           "Glass Shield, The (1994)",
+           2
+          ],
+          [
+           1552,
+           "Hunted, The (1995)",
+           3
+          ],
+          [
+           1553,
+           "Underneath, The (1995)",
+           4
+          ],
+          [
+           1554,
+           "Safe Passage (1994)",
+           2
+          ],
+          [
+           1555,
+           "Secret Adventures of Tom Thumb, The (1993)",
+           5
+          ],
+          [
+           1556,
+           "Condition Red (1995)",
+           2
+          ],
+          [
+           1557,
+           "Yankee Zulu (1994)",
+           1
+          ],
+          [
+           1558,
+           "Aparajito (1956)",
+           7
+          ],
+          [
+           1559,
+           "Hostile Intentions (1994)",
+           1
+          ],
+          [
+           1560,
+           "Clean Slate (Coup de Torchon) (1981)",
+           4
+          ],
+          [
+           1561,
+           "Tigrero: A Film That Was Never Made (1994)",
+           1
+          ],
+          [
+           1562,
+           "Eye of Vichy, The (Oeil de Vichy, L') (1993)",
+           1
+          ],
+          [
+           1563,
+           "Promise, The (Versprechen, Das) (1994)",
+           1
+          ],
+          [
+           1564,
+           "To Cross the Rubicon (1991)",
+           1
+          ],
+          [
+           1565,
+           "Daens (1992)",
+           1
+          ],
+          [
+           1566,
+           "Man from Down Under, The (1943)",
+           1
+          ],
+          [
+           1567,
+           "Careful (1992)",
+           1
+          ],
+          [
+           1568,
+           "Vermont Is For Lovers (1992)",
+           1
+          ],
+          [
+           1569,
+           "Vie est belle, La (Life is Rosey) (1987)",
+           1
+          ],
+          [
+           1570,
+           "Quartier Mozart (1992)",
+           1
+          ],
+          [
+           1571,
+           "Touki Bouki (Journey of the Hyena) (1973)",
+           1
+          ],
+          [
+           1572,
+           "Wend Kuuni (God's Gift) (1982)",
+           1
+          ],
+          [
+           1573,
+           "Spirits of the Dead (Tre passi nel delirio) (1968)",
+           2
+          ],
+          [
+           1574,
+           "Pharaoh's Army (1995)",
+           1
+          ],
+          [
+           1575,
+           "I, Worst of All (Yo, la peor de todas) (1990)",
+           1
+          ],
+          [
+           1576,
+           "Hungarian Fairy Tale, A (1987)",
+           1
+          ],
+          [
+           1577,
+           "Death in the Garden (Mort en ce jardin, La) (1956)",
+           1
+          ],
+          [
+           1578,
+           "Collectionneuse, La (1967)",
+           2
+          ],
+          [
+           1579,
+           "Baton Rouge (1988)",
+           1
+          ],
+          [
+           1580,
+           "Liebelei (1933)",
+           1
+          ],
+          [
+           1581,
+           "Woman in Question, The (1950)",
+           1
+          ],
+          [
+           1582,
+           "T-Men (1947)",
+           1
+          ],
+          [
+           1583,
+           "Invitation, The (Zaproszenie) (1986)",
+           1
+          ],
+          [
+           1584,
+           "Symphonie pastorale, La (1946)",
+           1
+          ],
+          [
+           1585,
+           "American Dream (1990)",
+           2
+          ],
+          [
+           1586,
+           "Lashou shentan (1992)",
+           1
+          ],
+          [
+           1587,
+           "Terror in a Texas Town (1958)",
+           1
+          ],
+          [
+           1588,
+           "Salut cousin! (1996)",
+           2
+          ],
+          [
+           1589,
+           "Schizopolis (1996)",
+           4
+          ],
+          [
+           1590,
+           "To Have, or Not (1995)",
+           2
+          ],
+          [
+           1591,
+           "Duoluo tianshi (1995)",
+           6
+          ],
+          [
+           1592,
+           "Magic Hour, The (1998)",
+           5
+          ],
+          [
+           1593,
+           "Death in Brunswick (1991)",
+           1
+          ],
+          [
+           1594,
+           "Everest (1998)",
+           2
+          ],
+          [
+           1595,
+           "Shopping (1994)",
+           1
+          ],
+          [
+           1596,
+           "Nemesis 2: Nebula (1995)",
+           1
+          ],
+          [
+           1597,
+           "Romper Stomper (1992)",
+           5
+          ],
+          [
+           1598,
+           "City of Industry (1997)",
+           6
+          ],
+          [
+           1599,
+           "Someone Else's America (1995)",
+           1
+          ],
+          [
+           1600,
+           "Guantanamera (1994)",
+           4
+          ],
+          [
+           1601,
+           "Office Killer (1997)",
+           1
+          ],
+          [
+           1602,
+           "Price Above Rubies, A (1998)",
+           3
+          ],
+          [
+           1603,
+           "Angela (1995)",
+           1
+          ],
+          [
+           1604,
+           "He Walked by Night (1948)",
+           1
+          ],
+          [
+           1605,
+           "Love Serenade (1996)",
+           4
+          ],
+          [
+           1606,
+           "Deceiver (1997)",
+           1
+          ],
+          [
+           1607,
+           "Hurricane Streets (1998)",
+           3
+          ],
+          [
+           1608,
+           "Buddy (1997)",
+           4
+          ],
+          [
+           1609,
+           "B*A*P*S (1997)",
+           3
+          ],
+          [
+           1610,
+           "Truth or Consequences, N.M. (1997)",
+           3
+          ],
+          [
+           1611,
+           "Intimate Relations (1996)",
+           2
+          ],
+          [
+           1612,
+           "Leading Man, The (1996)",
+           4
+          ],
+          [
+           1613,
+           "Tokyo Fist (1995)",
+           1
+          ],
+          [
+           1614,
+           "Reluctant Debutante, The (1958)",
+           1
+          ],
+          [
+           1615,
+           "Warriors of Virtue (1997)",
+           10
+          ],
+          [
+           1616,
+           "Desert Winds (1995)",
+           1
+          ],
+          [
+           1617,
+           "Hugo Pool (1997)",
+           2
+          ],
+          [
+           1618,
+           "King of New York (1990)",
+           1
+          ],
+          [
+           1619,
+           "All Things Fair (1996)",
+           1
+          ],
+          [
+           1620,
+           "Sixth Man, The (1997)",
+           9
+          ],
+          [
+           1621,
+           "Butterfly Kiss (1995)",
+           1
+          ],
+          [
+           1622,
+           "Paris, France (1993)",
+           3
+          ],
+          [
+           1623,
+           "Cérémonie, La (1995)",
+           3
+          ],
+          [
+           1624,
+           "Hush (1998)",
+           1
+          ],
+          [
+           1625,
+           "Nightwatch (1997)",
+           1
+          ],
+          [
+           1626,
+           "Nobody Loves Me (Keiner liebt mich) (1994)",
+           1
+          ],
+          [
+           1627,
+           "Wife, The (1995)",
+           1
+          ],
+          [
+           1628,
+           "Lamerica (1994)",
+           4
+          ],
+          [
+           1629,
+           "Nico Icon (1995)",
+           2
+          ],
+          [
+           1630,
+           "Silence of the Palace, The (Saimt el Qusur) (1994)",
+           1
+          ],
+          [
+           1631,
+           "Slingshot, The (1993)",
+           2
+          ],
+          [
+           1632,
+           "Land and Freedom (Tierra y libertad) (1995)",
+           1
+          ],
+          [
+           1633,
+           "Á köldum klaka (Cold Fever) (1994)",
+           1
+          ],
+          [
+           1634,
+           "Etz Hadomim Tafus (Under the Domin Tree) (1994)",
+           1
+          ],
+          [
+           1635,
+           "Two Friends (1986) ",
+           1
+          ],
+          [
+           1636,
+           "Brothers in Trouble (1995)",
+           1
+          ],
+          [
+           1637,
+           "Girls Town (1996)",
+           1
+          ],
+          [
+           1638,
+           "Normal Life (1996)",
+           1
+          ],
+          [
+           1639,
+           "Bitter Sugar (Azucar Amargo) (1996)",
+           3
+          ],
+          [
+           1640,
+           "Eighth Day, The (1996)",
+           1
+          ],
+          [
+           1641,
+           "Dadetown (1995)",
+           1
+          ],
+          [
+           1642,
+           "Some Mother's Son (1996)",
+           2
+          ],
+          [
+           1643,
+           "Angel Baby (1995)",
+           4
+          ],
+          [
+           1644,
+           "Sudden Manhattan (1996)",
+           2
+          ],
+          [
+           1645,
+           "Butcher Boy, The (1998)",
+           1
+          ],
+          [
+           1646,
+           "Men With Guns (1997)",
+           2
+          ],
+          [
+           1647,
+           "Hana-bi (1997)",
+           1
+          ],
+          [
+           1648,
+           "Niagara, Niagara (1997)",
+           1
+          ],
+          [
+           1649,
+           "Big One, The (1997)",
+           1
+          ],
+          [
+           1650,
+           "Butcher Boy, The (1998)",
+           1
+          ],
+          [
+           1651,
+           "Spanish Prisoner, The (1997)",
+           1
+          ],
+          [
+           1652,
+           "Temptress Moon (Feng Yue) (1996)",
+           3
+          ],
+          [
+           1653,
+           "Entertaining Angels: The Dorothy Day Story (1996)",
+           1
+          ],
+          [
+           1654,
+           "Chairman of the Board (1998)",
+           1
+          ],
+          [
+           1655,
+           "Favor, The (1994)",
+           1
+          ],
+          [
+           1656,
+           "Little City (1998)",
+           2
+          ],
+          [
+           1657,
+           "Target (1995)",
+           1
+          ],
+          [
+           1658,
+           "Substance of Fire, The (1996)",
+           3
+          ],
+          [
+           1659,
+           "Getting Away With Murder (1996)",
+           1
+          ],
+          [
+           1660,
+           "Small Faces (1995)",
+           1
+          ],
+          [
+           1661,
+           "New Age, The (1994)",
+           1
+          ],
+          [
+           1662,
+           "Rough Magic (1995)",
+           2
+          ],
+          [
+           1663,
+           "Nothing Personal (1995)",
+           1
+          ],
+          [
+           1664,
+           "8 Heads in a Duffel Bag (1997)",
+           4
+          ],
+          [
+           1665,
+           "Brother's Kiss, A (1997)",
+           1
+          ],
+          [
+           1666,
+           "Ripe (1996)",
+           1
+          ],
+          [
+           1667,
+           "Next Step, The (1995)",
+           1
+          ],
+          [
+           1668,
+           "Wedding Bell Blues (1996)",
+           1
+          ],
+          [
+           1669,
+           "MURDER and murder (1996)",
+           1
+          ],
+          [
+           1670,
+           "Tainted (1998)",
+           1
+          ],
+          [
+           1671,
+           "Further Gesture, A (1996)",
+           1
+          ],
+          [
+           1672,
+           "Kika (1993)",
+           2
+          ],
+          [
+           1673,
+           "Mirage (1995)",
+           1
+          ],
+          [
+           1674,
+           "Mamma Roma (1962)",
+           1
+          ],
+          [
+           1675,
+           "Sunchaser, The (1996)",
+           1
+          ],
+          [
+           1676,
+           "War at Home, The (1996)",
+           1
+          ],
+          [
+           1677,
+           "Sweet Nothing (1995)",
+           1
+          ],
+          [
+           1678,
+           "Mat' i syn (1997)",
+           1
+          ],
+          [
+           1679,
+           "B. Monkey (1998)",
+           1
+          ],
+          [
+           1680,
+           "Sliding Doors (1998)",
+           1
+          ],
+          [
+           1681,
+           "You So Crazy (1994)",
+           1
+          ],
+          [
+           1682,
+           "Scream of Stone (Schrei aus Stein) (1991)",
+           1
+          ]
+         ],
+         "hovertemplate": "tsne_1=%{x}<br>tsne_2=%{y}<br>item_id=%{customdata[0]}<br>title=%{customdata[1]}<br>popularity=%{marker.color}<extra></extra>",
+         "legendgroup": "",
+         "marker": {
+          "color": {
+           "bdata": "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",
+           "dtype": "i2"
+          },
+          "coloraxis": "coloraxis",
+          "symbol": "circle"
+         },
+         "mode": "markers",
+         "name": "",
+         "showlegend": false,
+         "type": "scattergl",
+         "x": {
+          "bdata": "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",
+          "dtype": "f4"
+         },
+         "xaxis": "x",
+         "y": {
+          "bdata": "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",
+          "dtype": "f4"
+         },
+         "yaxis": "y"
+        }
+       ],
+       "layout": {
+        "coloraxis": {
+         "colorbar": {
+          "title": {
+           "text": "popularity"
+          }
+         },
+         "colorscale": [
+          [
+           0,
+           "#0d0887"
+          ],
+          [
+           0.1111111111111111,
+           "#46039f"
+          ],
+          [
+           0.2222222222222222,
+           "#7201a8"
+          ],
+          [
+           0.3333333333333333,
+           "#9c179e"
+          ],
+          [
+           0.4444444444444444,
+           "#bd3786"
+          ],
+          [
+           0.5555555555555556,
+           "#d8576b"
+          ],
+          [
+           0.6666666666666666,
+           "#ed7953"
+          ],
+          [
+           0.7777777777777778,
+           "#fb9f3a"
+          ],
+          [
+           0.8888888888888888,
+           "#fdca26"
+          ],
+          [
+           1,
+           "#f0f921"
+          ]
+         ]
+        },
+        "legend": {
+         "tracegroupgap": 0
+        },
+        "margin": {
+         "t": 60
+        },
+        "template": {
+         "data": {
+          "bar": [
+           {
+            "error_x": {
+             "color": "#2a3f5f"
+            },
+            "error_y": {
+             "color": "#2a3f5f"
+            },
+            "marker": {
+             "line": {
+              "color": "#E5ECF6",
+              "width": 0.5
+             },
+             "pattern": {
+              "fillmode": "overlay",
+              "size": 10,
+              "solidity": 0.2
+             }
+            },
+            "type": "bar"
+           }
+          ],
+          "barpolar": [
+           {
+            "marker": {
+             "line": {
+              "color": "#E5ECF6",
+              "width": 0.5
+             },
+             "pattern": {
+              "fillmode": "overlay",
+              "size": 10,
+              "solidity": 0.2
+             }
+            },
+            "type": "barpolar"
+           }
+          ],
+          "carpet": [
+           {
+            "aaxis": {
+             "endlinecolor": "#2a3f5f",
+             "gridcolor": "white",
+             "linecolor": "white",
+             "minorgridcolor": "white",
+             "startlinecolor": "#2a3f5f"
+            },
+            "baxis": {
+             "endlinecolor": "#2a3f5f",
+             "gridcolor": "white",
+             "linecolor": "white",
+             "minorgridcolor": "white",
+             "startlinecolor": "#2a3f5f"
+            },
+            "type": "carpet"
+           }
+          ],
+          "choropleth": [
+           {
+            "colorbar": {
+             "outlinewidth": 0,
+             "ticks": ""
+            },
+            "type": "choropleth"
+           }
+          ],
+          "contour": [
+           {
+            "colorbar": {
+             "outlinewidth": 0,
+             "ticks": ""
+            },
+            "colorscale": [
+             [
+              0,
+              "#0d0887"
+             ],
+             [
+              0.1111111111111111,
+              "#46039f"
+             ],
+             [
+              0.2222222222222222,
+              "#7201a8"
+             ],
+             [
+              0.3333333333333333,
+              "#9c179e"
+             ],
+             [
+              0.4444444444444444,
+              "#bd3786"
+             ],
+             [
+              0.5555555555555556,
+              "#d8576b"
+             ],
+             [
+              0.6666666666666666,
+              "#ed7953"
+             ],
+             [
+              0.7777777777777778,
+              "#fb9f3a"
+             ],
+             [
+              0.8888888888888888,
+              "#fdca26"
+             ],
+             [
+              1,
+              "#f0f921"
+             ]
+            ],
+            "type": "contour"
+           }
+          ],
+          "contourcarpet": [
+           {
+            "colorbar": {
+             "outlinewidth": 0,
+             "ticks": ""
+            },
+            "type": "contourcarpet"
+           }
+          ],
+          "heatmap": [
+           {
+            "colorbar": {
+             "outlinewidth": 0,
+             "ticks": ""
+            },
+            "colorscale": [
+             [
+              0,
+              "#0d0887"
+             ],
+             [
+              0.1111111111111111,
+              "#46039f"
+             ],
+             [
+              0.2222222222222222,
+              "#7201a8"
+             ],
+             [
+              0.3333333333333333,
+              "#9c179e"
+             ],
+             [
+              0.4444444444444444,
+              "#bd3786"
+             ],
+             [
+              0.5555555555555556,
+              "#d8576b"
+             ],
+             [
+              0.6666666666666666,
+              "#ed7953"
+             ],
+             [
+              0.7777777777777778,
+              "#fb9f3a"
+             ],
+             [
+              0.8888888888888888,
+              "#fdca26"
+             ],
+             [
+              1,
+              "#f0f921"
+             ]
+            ],
+            "type": "heatmap"
+           }
+          ],
+          "histogram": [
+           {
+            "marker": {
+             "pattern": {
+              "fillmode": "overlay",
+              "size": 10,
+              "solidity": 0.2
+             }
+            },
+            "type": "histogram"
+           }
+          ],
+          "histogram2d": [
+           {
+            "colorbar": {
+             "outlinewidth": 0,
+             "ticks": ""
+            },
+            "colorscale": [
+             [
+              0,
+              "#0d0887"
+             ],
+             [
+              0.1111111111111111,
+              "#46039f"
+             ],
+             [
+              0.2222222222222222,
+              "#7201a8"
+             ],
+             [
+              0.3333333333333333,
+              "#9c179e"
+             ],
+             [
+              0.4444444444444444,
+              "#bd3786"
+             ],
+             [
+              0.5555555555555556,
+              "#d8576b"
+             ],
+             [
+              0.6666666666666666,
+              "#ed7953"
+             ],
+             [
+              0.7777777777777778,
+              "#fb9f3a"
+             ],
+             [
+              0.8888888888888888,
+              "#fdca26"
+             ],
+             [
+              1,
+              "#f0f921"
+             ]
+            ],
+            "type": "histogram2d"
+           }
+          ],
+          "histogram2dcontour": [
+           {
+            "colorbar": {
+             "outlinewidth": 0,
+             "ticks": ""
+            },
+            "colorscale": [
+             [
+              0,
+              "#0d0887"
+             ],
+             [
+              0.1111111111111111,
+              "#46039f"
+             ],
+             [
+              0.2222222222222222,
+              "#7201a8"
+             ],
+             [
+              0.3333333333333333,
+              "#9c179e"
+             ],
+             [
+              0.4444444444444444,
+              "#bd3786"
+             ],
+             [
+              0.5555555555555556,
+              "#d8576b"
+             ],
+             [
+              0.6666666666666666,
+              "#ed7953"
+             ],
+             [
+              0.7777777777777778,
+              "#fb9f3a"
+             ],
+             [
+              0.8888888888888888,
+              "#fdca26"
+             ],
+             [
+              1,
+              "#f0f921"
+             ]
+            ],
+            "type": "histogram2dcontour"
+           }
+          ],
+          "mesh3d": [
+           {
+            "colorbar": {
+             "outlinewidth": 0,
+             "ticks": ""
+            },
+            "type": "mesh3d"
+           }
+          ],
+          "parcoords": [
+           {
+            "line": {
+             "colorbar": {
+              "outlinewidth": 0,
+              "ticks": ""
+             }
+            },
+            "type": "parcoords"
+           }
+          ],
+          "pie": [
+           {
+            "automargin": true,
+            "type": "pie"
+           }
+          ],
+          "scatter": [
+           {
+            "fillpattern": {
+             "fillmode": "overlay",
+             "size": 10,
+             "solidity": 0.2
+            },
+            "type": "scatter"
+           }
+          ],
+          "scatter3d": [
+           {
+            "line": {
+             "colorbar": {
+              "outlinewidth": 0,
+              "ticks": ""
+             }
+            },
+            "marker": {
+             "colorbar": {
+              "outlinewidth": 0,
+              "ticks": ""
+             }
+            },
+            "type": "scatter3d"
+           }
+          ],
+          "scattercarpet": [
+           {
+            "marker": {
+             "colorbar": {
+              "outlinewidth": 0,
+              "ticks": ""
+             }
+            },
+            "type": "scattercarpet"
+           }
+          ],
+          "scattergeo": [
+           {
+            "marker": {
+             "colorbar": {
+              "outlinewidth": 0,
+              "ticks": ""
+             }
+            },
+            "type": "scattergeo"
+           }
+          ],
+          "scattergl": [
+           {
+            "marker": {
+             "colorbar": {
+              "outlinewidth": 0,
+              "ticks": ""
+             }
+            },
+            "type": "scattergl"
+           }
+          ],
+          "scattermap": [
+           {
+            "marker": {
+             "colorbar": {
+              "outlinewidth": 0,
+              "ticks": ""
+             }
+            },
+            "type": "scattermap"
+           }
+          ],
+          "scattermapbox": [
+           {
+            "marker": {
+             "colorbar": {
+              "outlinewidth": 0,
+              "ticks": ""
+             }
+            },
+            "type": "scattermapbox"
+           }
+          ],
+          "scatterpolar": [
+           {
+            "marker": {
+             "colorbar": {
+              "outlinewidth": 0,
+              "ticks": ""
+             }
+            },
+            "type": "scatterpolar"
+           }
+          ],
+          "scatterpolargl": [
+           {
+            "marker": {
+             "colorbar": {
+              "outlinewidth": 0,
+              "ticks": ""
+             }
+            },
+            "type": "scatterpolargl"
+           }
+          ],
+          "scatterternary": [
+           {
+            "marker": {
+             "colorbar": {
+              "outlinewidth": 0,
+              "ticks": ""
+             }
+            },
+            "type": "scatterternary"
+           }
+          ],
+          "surface": [
+           {
+            "colorbar": {
+             "outlinewidth": 0,
+             "ticks": ""
+            },
+            "colorscale": [
+             [
+              0,
+              "#0d0887"
+             ],
+             [
+              0.1111111111111111,
+              "#46039f"
+             ],
+             [
+              0.2222222222222222,
+              "#7201a8"
+             ],
+             [
+              0.3333333333333333,
+              "#9c179e"
+             ],
+             [
+              0.4444444444444444,
+              "#bd3786"
+             ],
+             [
+              0.5555555555555556,
+              "#d8576b"
+             ],
+             [
+              0.6666666666666666,
+              "#ed7953"
+             ],
+             [
+              0.7777777777777778,
+              "#fb9f3a"
+             ],
+             [
+              0.8888888888888888,
+              "#fdca26"
+             ],
+             [
+              1,
+              "#f0f921"
+             ]
+            ],
+            "type": "surface"
+           }
+          ],
+          "table": [
+           {
+            "cells": {
+             "fill": {
+              "color": "#EBF0F8"
+             },
+             "line": {
+              "color": "white"
+             }
+            },
+            "header": {
+             "fill": {
+              "color": "#C8D4E3"
+             },
+             "line": {
+              "color": "white"
+             }
+            },
+            "type": "table"
+           }
+          ]
+         },
+         "layout": {
+          "annotationdefaults": {
+           "arrowcolor": "#2a3f5f",
+           "arrowhead": 0,
+           "arrowwidth": 1
+          },
+          "autotypenumbers": "strict",
+          "coloraxis": {
+           "colorbar": {
+            "outlinewidth": 0,
+            "ticks": ""
+           }
+          },
+          "colorscale": {
+           "diverging": [
+            [
+             0,
+             "#8e0152"
+            ],
+            [
+             0.1,
+             "#c51b7d"
+            ],
+            [
+             0.2,
+             "#de77ae"
+            ],
+            [
+             0.3,
+             "#f1b6da"
+            ],
+            [
+             0.4,
+             "#fde0ef"
+            ],
+            [
+             0.5,
+             "#f7f7f7"
+            ],
+            [
+             0.6,
+             "#e6f5d0"
+            ],
+            [
+             0.7,
+             "#b8e186"
+            ],
+            [
+             0.8,
+             "#7fbc41"
+            ],
+            [
+             0.9,
+             "#4d9221"
+            ],
+            [
+             1,
+             "#276419"
+            ]
+           ],
+           "sequential": [
+            [
+             0,
+             "#0d0887"
+            ],
+            [
+             0.1111111111111111,
+             "#46039f"
+            ],
+            [
+             0.2222222222222222,
+             "#7201a8"
+            ],
+            [
+             0.3333333333333333,
+             "#9c179e"
+            ],
+            [
+             0.4444444444444444,
+             "#bd3786"
+            ],
+            [
+             0.5555555555555556,
+             "#d8576b"
+            ],
+            [
+             0.6666666666666666,
+             "#ed7953"
+            ],
+            [
+             0.7777777777777778,
+             "#fb9f3a"
+            ],
+            [
+             0.8888888888888888,
+             "#fdca26"
+            ],
+            [
+             1,
+             "#f0f921"
+            ]
+           ],
+           "sequentialminus": [
+            [
+             0,
+             "#0d0887"
+            ],
+            [
+             0.1111111111111111,
+             "#46039f"
+            ],
+            [
+             0.2222222222222222,
+             "#7201a8"
+            ],
+            [
+             0.3333333333333333,
+             "#9c179e"
+            ],
+            [
+             0.4444444444444444,
+             "#bd3786"
+            ],
+            [
+             0.5555555555555556,
+             "#d8576b"
+            ],
+            [
+             0.6666666666666666,
+             "#ed7953"
+            ],
+            [
+             0.7777777777777778,
+             "#fb9f3a"
+            ],
+            [
+             0.8888888888888888,
+             "#fdca26"
+            ],
+            [
+             1,
+             "#f0f921"
+            ]
+           ]
+          },
+          "colorway": [
+           "#636efa",
+           "#EF553B",
+           "#00cc96",
+           "#ab63fa",
+           "#FFA15A",
+           "#19d3f3",
+           "#FF6692",
+           "#B6E880",
+           "#FF97FF",
+           "#FECB52"
+          ],
+          "font": {
+           "color": "#2a3f5f"
+          },
+          "geo": {
+           "bgcolor": "white",
+           "lakecolor": "white",
+           "landcolor": "#E5ECF6",
+           "showlakes": true,
+           "showland": true,
+           "subunitcolor": "white"
+          },
+          "hoverlabel": {
+           "align": "left"
+          },
+          "hovermode": "closest",
+          "mapbox": {
+           "style": "light"
+          },
+          "paper_bgcolor": "white",
+          "plot_bgcolor": "#E5ECF6",
+          "polar": {
+           "angularaxis": {
+            "gridcolor": "white",
+            "linecolor": "white",
+            "ticks": ""
+           },
+           "bgcolor": "#E5ECF6",
+           "radialaxis": {
+            "gridcolor": "white",
+            "linecolor": "white",
+            "ticks": ""
+           }
+          },
+          "scene": {
+           "xaxis": {
+            "backgroundcolor": "#E5ECF6",
+            "gridcolor": "white",
+            "gridwidth": 2,
+            "linecolor": "white",
+            "showbackground": true,
+            "ticks": "",
+            "zerolinecolor": "white"
+           },
+           "yaxis": {
+            "backgroundcolor": "#E5ECF6",
+            "gridcolor": "white",
+            "gridwidth": 2,
+            "linecolor": "white",
+            "showbackground": true,
+            "ticks": "",
+            "zerolinecolor": "white"
+           },
+           "zaxis": {
+            "backgroundcolor": "#E5ECF6",
+            "gridcolor": "white",
+            "gridwidth": 2,
+            "linecolor": "white",
+            "showbackground": true,
+            "ticks": "",
+            "zerolinecolor": "white"
+           }
+          },
+          "shapedefaults": {
+           "line": {
+            "color": "#2a3f5f"
+           }
+          },
+          "ternary": {
+           "aaxis": {
+            "gridcolor": "white",
+            "linecolor": "white",
+            "ticks": ""
+           },
+           "baxis": {
+            "gridcolor": "white",
+            "linecolor": "white",
+            "ticks": ""
+           },
+           "bgcolor": "#E5ECF6",
+           "caxis": {
+            "gridcolor": "white",
+            "linecolor": "white",
+            "ticks": ""
+           }
+          },
+          "title": {
+           "x": 0.05
+          },
+          "xaxis": {
+           "automargin": true,
+           "gridcolor": "white",
+           "linecolor": "white",
+           "ticks": "",
+           "title": {
+            "standoff": 15
+           },
+           "zerolinecolor": "white",
+           "zerolinewidth": 2
+          },
+          "yaxis": {
+           "automargin": true,
+           "gridcolor": "white",
+           "linecolor": "white",
+           "ticks": "",
+           "title": {
+            "standoff": 15
+           },
+           "zerolinecolor": "white",
+           "zerolinewidth": 2
+          }
+         }
+        },
+        "xaxis": {
+         "anchor": "y",
+         "domain": [
+          0,
+          1
+         ],
+         "title": {
+          "text": "tsne_1"
+         }
+        },
+        "yaxis": {
+         "anchor": "x",
+         "domain": [
+          0,
+          1
+         ],
+         "title": {
+          "text": "tsne_2"
+         }
+        }
+       }
+      }
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "import plotly.express as px\n",
     "\n",
@@ -746,7 +11203,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 38,
    "metadata": {
     "scrolled": true
    },
@@ -768,9 +11225,27 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 39,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[1m48/48\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step\n",
+      "    4.4: Eat Drink Man Woman (1994)\n",
+      "    4.3: 20,000 Leagues Under the Sea (1954)\n",
+      "    4.1: Get Shorty (1995)\n",
+      "    4.1: Dead Man Walking (1995)\n",
+      "    4.1: Faster Pussycat! Kill! Kill! (1965)\n",
+      "    4.1: Kazaam (1996)\n",
+      "    4.0: Celtic Pride (1996)\n",
+      "    4.0: Silence of the Lambs, The (1991)\n",
+      "    4.0: Robert A. Heinlein's The Puppet Masters (1994)\n",
+      "    4.0: Color of Night (1994)\n"
+     ]
+    }
+   ],
    "source": [
     "for title, pred_rating in recommend(5):\n",
     "    print(\"    %0.1f: %s\" % (pred_rating, title))"
@@ -790,7 +11265,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 40,
    "metadata": {
     "collapsed": false
    },
@@ -829,11 +11304,38 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 41,
    "metadata": {
     "collapsed": false
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 1/10\n",
+      "\u001b[1m1125/1125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 2ms/step - loss: 2.6694 - val_loss: 1.0652\n",
+      "Epoch 2/10\n",
+      "\u001b[1m1125/1125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 2ms/step - loss: 0.8554 - val_loss: 0.7938\n",
+      "Epoch 3/10\n",
+      "\u001b[1m1125/1125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 2ms/step - loss: 0.7522 - val_loss: 0.7682\n",
+      "Epoch 4/10\n",
+      "\u001b[1m1125/1125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 2ms/step - loss: 0.7206 - val_loss: 0.7479\n",
+      "Epoch 5/10\n",
+      "\u001b[1m1125/1125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 2ms/step - loss: 0.6967 - val_loss: 0.7470\n",
+      "Epoch 6/10\n",
+      "\u001b[1m1125/1125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 2ms/step - loss: 0.6752 - val_loss: 0.7378\n",
+      "Epoch 7/10\n",
+      "\u001b[1m1125/1125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 3ms/step - loss: 0.6535 - val_loss: 0.7359\n",
+      "Epoch 8/10\n",
+      "\u001b[1m1125/1125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 2ms/step - loss: 0.6307 - val_loss: 0.7391\n",
+      "Epoch 9/10\n",
+      "\u001b[1m1125/1125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 3ms/step - loss: 0.6079 - val_loss: 0.7391\n",
+      "Epoch 10/10\n",
+      "\u001b[1m1125/1125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 2ms/step - loss: 0.5833 - val_loss: 0.7414\n"
+     ]
+    }
+   ],
    "source": [
     "# Training the model\n",
     "history = model.fit([user_id_train, item_id_train], rating_train,\n",
@@ -843,17 +11345,110 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 42,
    "metadata": {},
-   "outputs": [],
-   "source": []
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 1/20\n",
+      "\u001b[1m1125/1125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 3ms/step - loss: 0.5571 - val_loss: 0.7457\n",
+      "Epoch 2/20\n",
+      "\u001b[1m1125/1125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 3ms/step - loss: 0.5308 - val_loss: 0.7449\n",
+      "Epoch 3/20\n",
+      "\u001b[1m1125/1125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 2ms/step - loss: 0.5035 - val_loss: 0.7524\n",
+      "Epoch 4/20\n",
+      "\u001b[1m1125/1125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 2ms/step - loss: 0.4772 - val_loss: 0.7574\n",
+      "Epoch 5/20\n",
+      "\u001b[1m1125/1125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 2ms/step - loss: 0.4517 - val_loss: 0.7575\n",
+      "Epoch 6/20\n",
+      "\u001b[1m1125/1125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 2ms/step - loss: 0.4275 - val_loss: 0.7617\n",
+      "Epoch 7/20\n",
+      "\u001b[1m1125/1125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 2ms/step - loss: 0.4036 - val_loss: 0.7648\n",
+      "Epoch 8/20\n",
+      "\u001b[1m1125/1125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 2ms/step - loss: 0.3821 - val_loss: 0.7715\n",
+      "Epoch 9/20\n",
+      "\u001b[1m1125/1125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 3ms/step - loss: 0.3619 - val_loss: 0.7787\n",
+      "Epoch 10/20\n",
+      "\u001b[1m1125/1125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 2ms/step - loss: 0.3433 - val_loss: 0.7814\n",
+      "Epoch 11/20\n",
+      "\u001b[1m1125/1125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 2ms/step - loss: 0.3262 - val_loss: 0.7833\n",
+      "Epoch 12/20\n",
+      "\u001b[1m1125/1125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 2ms/step - loss: 0.3112 - val_loss: 0.7931\n",
+      "Epoch 13/20\n",
+      "\u001b[1m1125/1125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 2ms/step - loss: 0.2972 - val_loss: 0.7930\n",
+      "Epoch 14/20\n",
+      "\u001b[1m1125/1125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 2ms/step - loss: 0.2832 - val_loss: 0.8010\n",
+      "Epoch 15/20\n",
+      "\u001b[1m1125/1125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 2ms/step - loss: 0.2721 - val_loss: 0.8060\n",
+      "Epoch 16/20\n",
+      "\u001b[1m1125/1125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 2ms/step - loss: 0.2609 - val_loss: 0.8113\n",
+      "Epoch 17/20\n",
+      "\u001b[1m1125/1125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 2ms/step - loss: 0.2513 - val_loss: 0.8139\n",
+      "Epoch 18/20\n",
+      "\u001b[1m1125/1125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 2ms/step - loss: 0.2426 - val_loss: 0.8178\n",
+      "Epoch 19/20\n",
+      "\u001b[1m1125/1125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 2ms/step - loss: 0.2343 - val_loss: 0.8224\n",
+      "Epoch 20/20\n",
+      "\u001b[1m1125/1125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 2ms/step - loss: 0.2272 - val_loss: 0.8279\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Training the model\n",
+    "history = model.fit([user_id_train, item_id_train], rating_train,\n",
+    "                    batch_size=64, epochs=20, validation_split=0.1,\n",
+    "                    shuffle=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 1/11\n",
+      "\u001b[1m1125/1125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 2ms/step - loss: 0.2203 - val_loss: 0.8314\n",
+      "Epoch 2/11\n",
+      "\u001b[1m1125/1125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 2ms/step - loss: 0.2134 - val_loss: 0.8351\n",
+      "Epoch 3/11\n",
+      "\u001b[1m1125/1125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 2ms/step - loss: 0.2081 - val_loss: 0.8397\n",
+      "Epoch 4/11\n",
+      "\u001b[1m1125/1125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 2ms/step - loss: 0.2025 - val_loss: 0.8433\n",
+      "Epoch 5/11\n",
+      "\u001b[1m1125/1125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 2ms/step - loss: 0.1981 - val_loss: 0.8485\n",
+      "Epoch 6/11\n",
+      "\u001b[1m1125/1125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 2ms/step - loss: 0.1932 - val_loss: 0.8513\n",
+      "Epoch 7/11\n",
+      "\u001b[1m1125/1125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 2ms/step - loss: 0.1886 - val_loss: 0.8558\n",
+      "Epoch 8/11\n",
+      "\u001b[1m1125/1125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 2ms/step - loss: 0.1853 - val_loss: 0.8593\n",
+      "Epoch 9/11\n",
+      "\u001b[1m1125/1125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 2ms/step - loss: 0.1813 - val_loss: 0.8641\n",
+      "Epoch 10/11\n",
+      "\u001b[1m1125/1125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 2ms/step - loss: 0.1775 - val_loss: 0.8668\n",
+      "Epoch 11/11\n",
+      "\u001b[1m1125/1125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 2ms/step - loss: 0.1750 - val_loss: 0.8703\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Training the model\n",
+    "history = model.fit([user_id_train, item_id_train], rating_train,\n",
+    "                    batch_size=64, epochs=11, validation_split=0.1,\n",
+    "                    shuffle=True)"
+   ]
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "lab_1",
+   "display_name": "Deep Learning (.venv)",
    "language": "python",
-   "name": "python3"
+   "name": "deeplearning_venv"
   },
   "language_info": {
    "codemirror_mode": {
@@ -865,7 +11460,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.12.9"
+   "version": "3.11.0rc1"
   }
  },
  "nbformat": 4,
diff --git a/02_activities/assignments/assignment_1.ipynb b/02_activities/assignments/assignment_1.ipynb
index 6a1f05814..7862dc68b 100644
--- a/02_activities/assignments/assignment_1.ipynb
+++ b/02_activities/assignments/assignment_1.ipynb
@@ -29,10 +29,33 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "id": "420c7178",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2026-02-17 02:45:09.786587: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n",
+      "To enable the following instructions: AVX2 AVX512F FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/train-labels-idx1-ubyte.gz\n",
+      "\u001b[1m29515/29515\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 0us/step\n",
+      "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/train-images-idx3-ubyte.gz\n",
+      "\u001b[1m26421880/26421880\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 0us/step\n",
+      "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/t10k-labels-idx1-ubyte.gz\n",
+      "\u001b[1m5148/5148\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 0us/step\n",
+      "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/t10k-images-idx3-ubyte.gz\n",
+      "\u001b[1m4422102/4422102\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 0us/step\n"
+     ]
+    }
+   ],
    "source": [
     "from tensorflow.keras.datasets import fashion_mnist\n",
     "(X_train, y_train), (X_test, y_test) = fashion_mnist.load_data()\n",
@@ -47,28 +70,89 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 8,
    "id": "a6c89fe7",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "X_train shape: (60000, 28, 28, 1)\n",
+      "y_train shape: (60000,)\n",
+      "X_test shape: (10000, 28, 28, 1)\n",
+      "y_test shape: (10000,)\n",
+      "y_train shape after one-hot: (60000, 10)\n"
+     ]
+    }
+   ],
    "source": [
     "# Inspect the shapes of the datasets\n",
-    "\n",
+    "print(f\"X_train shape: {X_train.shape}\")\n",
+    "print(f\"y_train shape: {y_train.shape}\")\n",
+    "print(f\"X_test shape: {X_test.shape}\")\n",
+    "print(f\"y_test shape: {y_test.shape}\")\n",
     "\n",
     "# Convert labels to one-hot encoding\n",
     "from tensorflow.keras.utils import to_categorical\n",
+    "y_train = to_categorical(y_train, num_classes=10)\n",
+    "y_test = to_categorical(y_test, num_classes=10)\n",
+    "print(f\"y_train shape after one-hot: {y_train.shape}\")\n",
     "\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 9,
    "id": "13e100db",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 1000x1000 with 9 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Class distribution in training set:\n",
+      "  T-shirt/top: 6000\n",
+      "  Trouser: 6000\n",
+      "  Pullover: 6000\n",
+      "  Dress: 6000\n",
+      "  Coat: 6000\n",
+      "  Sandal: 6000\n",
+      "  Shirt: 6000\n",
+      "  Sneaker: 6000\n",
+      "  Bag: 6000\n",
+      "  Ankle boot: 6000\n"
+     ]
+    }
+   ],
    "source": [
     "import matplotlib.pyplot as plt\n",
-    "# Verify the data looks as expected\n"
+    "\n",
+    "# Plot some sample images\n",
+    "plt.figure(figsize=(10, 10))\n",
+    "for i in range(9):\n",
+    "    plt.subplot(3, 3, i+1)\n",
+    "    plt.imshow(X_train[i], cmap='gray')\n",
+    "    plt.title(class_names[y_train[i].argmax()])\n",
+    "    plt.axis('off')\n",
+    "plt.tight_layout()\n",
+    "plt.show()\n",
+    "\n",
+    "# Check class distribution\n",
+    "print(\"Class distribution in training set:\")\n",
+    "for i, name in enumerate(class_names):\n",
+    "    count = (y_train.argmax(axis=1) == i).sum()\n",
+    "    print(f\"  {name}: {count}\")\n"
    ]
   },
   {
@@ -78,7 +162,7 @@
    "source": [
     "Reflection: Does the data look as expected? How is the quality of the images? Are there any issues with the dataset that you notice?\n",
     "\n",
-    "**Your answer here**"
+    "The  quality of the images is poor..but that is by design to keep the model small... and I would hate to be the one evalutating this.  The top rigght pull over I would question...I would also suggest that the sandal is more likely high heels...but since that is not part of the class labels, sandal works...."
    ]
   },
   {
@@ -101,23 +185,151 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 13,
    "id": "8563a7aa",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"sequential_5\"</span>\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[1mModel: \"sequential_5\"\u001b[0m\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
+       "┃<span style=\"font-weight: bold\"> Layer (type)                    </span>┃<span style=\"font-weight: bold\"> Output Shape           </span>┃<span style=\"font-weight: bold\">       Param # </span>┃\n",
+       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
+       "│ flatten_2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Flatten</span>)             │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">784</span>)            │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ dense_3 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                 │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">10</span>)             │         <span style=\"color: #00af00; text-decoration-color: #00af00\">7,850</span> │\n",
+       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
+       "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                   \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape          \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m      Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
+       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
+       "│ flatten_2 (\u001b[38;5;33mFlatten\u001b[0m)             │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m784\u001b[0m)            │             \u001b[38;5;34m0\u001b[0m │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ dense_3 (\u001b[38;5;33mDense\u001b[0m)                 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m10\u001b[0m)             │         \u001b[38;5;34m7,850\u001b[0m │\n",
+       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">7,850</span> (30.66 KB)\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m7,850\u001b[0m (30.66 KB)\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">7,850</span> (30.66 KB)\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m7,850\u001b[0m (30.66 KB)\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 1/12\n",
+      "\u001b[1m1500/1500\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.7323 - loss: 0.8669 - val_accuracy: 0.7830 - val_loss: 0.6598\n",
+      "Epoch 2/12\n",
+      "\u001b[1m1500/1500\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 2ms/step - accuracy: 0.8008 - loss: 0.6145 - val_accuracy: 0.8047 - val_loss: 0.5822\n",
+      "Epoch 3/12\n",
+      "\u001b[1m1500/1500\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 2ms/step - accuracy: 0.8157 - loss: 0.5608 - val_accuracy: 0.8193 - val_loss: 0.5446\n",
+      "Epoch 4/12\n",
+      "\u001b[1m1500/1500\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 2ms/step - accuracy: 0.8252 - loss: 0.5315 - val_accuracy: 0.8234 - val_loss: 0.5206\n",
+      "Epoch 5/12\n",
+      "\u001b[1m1500/1500\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 2ms/step - accuracy: 0.8296 - loss: 0.5126 - val_accuracy: 0.8281 - val_loss: 0.5065\n",
+      "Epoch 6/12\n",
+      "\u001b[1m1500/1500\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 2ms/step - accuracy: 0.8328 - loss: 0.4989 - val_accuracy: 0.8290 - val_loss: 0.4989\n",
+      "Epoch 7/12\n",
+      "\u001b[1m1500/1500\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 2ms/step - accuracy: 0.8363 - loss: 0.4879 - val_accuracy: 0.8339 - val_loss: 0.4874\n",
+      "Epoch 8/12\n",
+      "\u001b[1m1500/1500\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 2ms/step - accuracy: 0.8396 - loss: 0.4795 - val_accuracy: 0.8344 - val_loss: 0.4807\n",
+      "Epoch 9/12\n",
+      "\u001b[1m1500/1500\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 2ms/step - accuracy: 0.8418 - loss: 0.4723 - val_accuracy: 0.8332 - val_loss: 0.4767\n",
+      "Epoch 10/12\n",
+      "\u001b[1m1500/1500\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 2ms/step - accuracy: 0.8441 - loss: 0.4663 - val_accuracy: 0.8382 - val_loss: 0.4701\n",
+      "Epoch 11/12\n",
+      "\u001b[1m1500/1500\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 2ms/step - accuracy: 0.8455 - loss: 0.4608 - val_accuracy: 0.8395 - val_loss: 0.4669\n",
+      "Epoch 12/12\n",
+      "\u001b[1m1500/1500\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 2ms/step - accuracy: 0.8457 - loss: 0.4564 - val_accuracy: 0.8388 - val_loss: 0.4652\n",
+      "\n",
+      "Baseline Model - Test accuracy: 0.8294\n"
+     ]
+    }
+   ],
    "source": [
     "from keras.models import Sequential\n",
     "from keras.layers import Dense, Flatten\n",
     "\n",
     "# Create a simple linear regression model\n",
     "model = Sequential()\n",
-    "# You can use `model.add(<layer>)` to add layers to the model\n",
+    "model.add(Flatten(input_shape=(28, 28)))  # Flatten 28x28 images to 784 vectors\n",
+    "model.add(Dense(10, activation='softmax'))  # 10 classes, softmax for probabilities\n",
     "\n",
-    "# Compile the model using `model.compile()`\n",
+    "# Compile the model\n",
+    "model.compile(\n",
+    "    loss='categorical_crossentropy',\n",
+    "    optimizer='sgd',\n",
+    "    metrics=['accuracy']\n",
+    ")\n",
     "\n",
-    "# Train the model with `model.fit()`\n",
+    "# Print model summary\n",
+    "model.summary()\n",
+    "\n",
+    "# Train the model\n",
+    "history = model.fit(\n",
+    "    X_train, y_train,\n",
+    "    epochs=12,\n",
+    "    batch_size=32,\n",
+    "    validation_split=0.2,\n",
+    "    verbose=1\n",
+    ")\n",
     "\n",
-    "# Evaluate the model with `model.evaluate()`"
+    "# Evaluate the model\n",
+    "test_loss, test_acc = model.evaluate(X_test, y_test, verbose=0)\n",
+    "print(f\"\\nBaseline Model - Test accuracy: {test_acc:.4f}\")"
    ]
   },
   {
@@ -127,7 +339,9 @@
    "source": [
     "Reflection: What is the performance of the baseline model? How does it compare to what you expected? Why do you think the performance is at this level?\n",
     "\n",
-    "**Your answer here**"
+    "**Your answer here**\n",
+    "\n",
+    "The model is at 83% test accuracy, which is very good for such a simple model...its linear with no hidden layers...it can only draw straight-line boudnaries between classes.  So clothing items are reasonably well-separatated by pixel space."
    ]
   },
   {
@@ -151,12 +365,148 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 12,
    "id": "3513cf3d",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/mnt/batch/tasks/shared/LS_root/mounts/clusters/dev-dsi/code/deep_learning/.venv/lib/python3.11/site-packages/keras/src/layers/convolutional/base_conv.py:113: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n",
+      "  super().__init__(activity_regularizer=activity_regularizer, **kwargs)\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"sequential_4\"</span>\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[1mModel: \"sequential_4\"\u001b[0m\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
+       "┃<span style=\"font-weight: bold\"> Layer (type)                    </span>┃<span style=\"font-weight: bold\"> Output Shape           </span>┃<span style=\"font-weight: bold\">       Param # </span>┃\n",
+       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
+       "│ conv2d (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)                 │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">26</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">26</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)     │           <span style=\"color: #00af00; text-decoration-color: #00af00\">320</span> │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ max_pooling2d (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>)    │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">13</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">13</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)     │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ conv2d_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)               │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">11</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">11</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)     │        <span style=\"color: #00af00; text-decoration-color: #00af00\">18,496</span> │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ max_pooling2d_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>)  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">5</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">5</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)       │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ flatten_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Flatten</span>)             │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1600</span>)           │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ dense_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                 │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)             │       <span style=\"color: #00af00; text-decoration-color: #00af00\">102,464</span> │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ dense_2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                 │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">10</span>)             │           <span style=\"color: #00af00; text-decoration-color: #00af00\">650</span> │\n",
+       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
+       "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                   \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape          \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m      Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
+       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
+       "│ conv2d (\u001b[38;5;33mConv2D\u001b[0m)                 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m26\u001b[0m, \u001b[38;5;34m26\u001b[0m, \u001b[38;5;34m32\u001b[0m)     │           \u001b[38;5;34m320\u001b[0m │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ max_pooling2d (\u001b[38;5;33mMaxPooling2D\u001b[0m)    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m13\u001b[0m, \u001b[38;5;34m13\u001b[0m, \u001b[38;5;34m32\u001b[0m)     │             \u001b[38;5;34m0\u001b[0m │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ conv2d_1 (\u001b[38;5;33mConv2D\u001b[0m)               │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m11\u001b[0m, \u001b[38;5;34m11\u001b[0m, \u001b[38;5;34m64\u001b[0m)     │        \u001b[38;5;34m18,496\u001b[0m │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ max_pooling2d_1 (\u001b[38;5;33mMaxPooling2D\u001b[0m)  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m64\u001b[0m)       │             \u001b[38;5;34m0\u001b[0m │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ flatten_1 (\u001b[38;5;33mFlatten\u001b[0m)             │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1600\u001b[0m)           │             \u001b[38;5;34m0\u001b[0m │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ dense_1 (\u001b[38;5;33mDense\u001b[0m)                 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m)             │       \u001b[38;5;34m102,464\u001b[0m │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ dense_2 (\u001b[38;5;33mDense\u001b[0m)                 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m10\u001b[0m)             │           \u001b[38;5;34m650\u001b[0m │\n",
+       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">121,930</span> (476.29 KB)\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m121,930\u001b[0m (476.29 KB)\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">121,930</span> (476.29 KB)\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m121,930\u001b[0m (476.29 KB)\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 1/50\n",
+      "\u001b[1m1500/1500\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m21s\u001b[0m 13ms/step - accuracy: 0.8166 - loss: 0.5002 - val_accuracy: 0.8631 - val_loss: 0.3738\n",
+      "Epoch 2/50\n",
+      "\u001b[1m1500/1500\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m19s\u001b[0m 13ms/step - accuracy: 0.8798 - loss: 0.3306 - val_accuracy: 0.8888 - val_loss: 0.3066\n",
+      "Epoch 3/50\n",
+      "\u001b[1m1500/1500\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m19s\u001b[0m 13ms/step - accuracy: 0.8971 - loss: 0.2806 - val_accuracy: 0.8956 - val_loss: 0.2823\n",
+      "Epoch 4/50\n",
+      "\u001b[1m1500/1500\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m19s\u001b[0m 13ms/step - accuracy: 0.9083 - loss: 0.2518 - val_accuracy: 0.9004 - val_loss: 0.2669\n",
+      "Epoch 5/50\n",
+      "\u001b[1m1500/1500\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m20s\u001b[0m 12ms/step - accuracy: 0.9174 - loss: 0.2221 - val_accuracy: 0.9046 - val_loss: 0.2557\n",
+      "Epoch 6/50\n",
+      "\u001b[1m1500/1500\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m19s\u001b[0m 12ms/step - accuracy: 0.9250 - loss: 0.2004 - val_accuracy: 0.9047 - val_loss: 0.2689\n",
+      "Epoch 7/50\n",
+      "\u001b[1m1500/1500\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m19s\u001b[0m 13ms/step - accuracy: 0.9329 - loss: 0.1807 - val_accuracy: 0.9037 - val_loss: 0.2768\n",
+      "Epoch 8/50\n",
+      "\u001b[1m1500/1500\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m19s\u001b[0m 13ms/step - accuracy: 0.9389 - loss: 0.1646 - val_accuracy: 0.9103 - val_loss: 0.2658\n",
+      "Epoch 9/50\n",
+      "\u001b[1m1500/1500\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m19s\u001b[0m 13ms/step - accuracy: 0.9448 - loss: 0.1467 - val_accuracy: 0.9092 - val_loss: 0.2757\n",
+      "Epoch 9: early stopping\n",
+      "Restoring model weights from the end of the best epoch: 5.\n",
+      "\n",
+      "CNN Model - Test accuracy: 0.9004\n",
+      "Stopped at epoch: 9\n"
+     ]
+    }
+   ],
    "source": [
-    "from keras.layers import Conv2D\n",
+    "from keras.layers import Conv2D, MaxPooling2D, Flatten, Dense\n",
+    "from keras.callbacks import EarlyStopping\n",
     "\n",
     "# Reshape the data to include the channel dimension\n",
     "X_train = X_train.reshape(-1, 28, 28, 1)\n",
@@ -165,9 +515,52 @@
     "# Create a simple CNN model\n",
     "model = Sequential()\n",
     "\n",
-    "# Train the model\n",
+    "# Add convolutional layers\n",
+    "model.add(Conv2D(32, (3, 3), activation='relu', input_shape=(28, 28, 1)))\n",
+    "model.add(MaxPooling2D((2, 2)))\n",
+    "\n",
+    "model.add(Conv2D(64, (3, 3), activation='relu'))\n",
+    "model.add(MaxPooling2D((2, 2)))\n",
+    "\n",
+    "# Flatten before dense layers\n",
+    "model.add(Flatten())\n",
     "\n",
-    "# Evaluate the model"
+    "# Add dense layers\n",
+    "model.add(Dense(64, activation='relu'))\n",
+    "model.add(Dense(10, activation='softmax'))\n",
+    "\n",
+    "# Show model summary\n",
+    "model.summary()\n",
+    "\n",
+    "# Compile the model\n",
+    "model.compile(\n",
+    "    optimizer='adam',\n",
+    "    loss='categorical_crossentropy',\n",
+    "    metrics=['accuracy']\n",
+    ")\n",
+    "\n",
+    "# Set up early stopping\n",
+    "early_stop = EarlyStopping(\n",
+    "    monitor='val_loss',      # Watch validation loss\n",
+    "    patience=4,               # Stop after 4 epochs with no improvement\n",
+    "    restore_best_weights=True, # Go back to best model\n",
+    "    verbose=1\n",
+    ")\n",
+    "\n",
+    "# Train the model with early stopping\n",
+    "history = model.fit(\n",
+    "    X_train, y_train,\n",
+    "    epochs=50,                # Set high - early stopping will halt early\n",
+    "    batch_size=32,\n",
+    "    validation_split=0.2,\n",
+    "    callbacks=[early_stop],   # Add the callback\n",
+    "    verbose=1\n",
+    ")\n",
+    "\n",
+    "# Evaluate the best model\n",
+    "test_loss, test_acc = model.evaluate(X_test, y_test, verbose=0)\n",
+    "print(f\"\\nCNN Model - Test accuracy: {test_acc:.4f}\")\n",
+    "print(f\"Stopped at epoch: {len(history.history['loss'])}\")"
    ]
   },
   {
@@ -177,7 +570,21 @@
    "source": [
     "Reflection: Did the CNN model perform better than the baseline model? If so, by how much? What do you think contributed to this improvement?\n",
     "\n",
-    "**Your answer here**"
+    "**Your answer here**\n",
+    "Baseline Model (Epoch 10):\n",
+    "\n",
+    "    Validation accuracy: 83.91%\n",
+    "\n",
+    "    Validation loss: 0.4695\n",
+    "\n",
+    "CNN Model (Best epoch 5):\n",
+    "\n",
+    "    Validation accuracy: 90.46%\n",
+    "\n",
+    "    Validation loss: 0.2557\n",
+    "\n",
+    "CNN was better in the first epoch.  The baseline model just flattens everything into pixels and draws straight lines between classes. \n",
+    "The CNN uses convolutional filters to find patterns like edges, textures, and shapes...things that actually matter for telling apart clothes."
    ]
   },
   {
@@ -201,22 +608,160 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 14,
    "id": "99d6f46c",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "🔍 Testing with 16 filters...\n",
+      "------------------------------\n",
+      "  Test accuracy: 0.8885\n",
+      "\n",
+      "🔍 Testing with 32 filters...\n",
+      "------------------------------\n",
+      "  Test accuracy: 0.9040\n",
+      "\n",
+      "🔍 Testing with 64 filters...\n",
+      "------------------------------\n",
+      "  Test accuracy: 0.9067\n",
+      "\n",
+      "============================================================\n",
+      "RESULTS\n",
+      "============================================================\n",
+      "Filters=16: 0.8885\n",
+      "Filters=32: 0.9040\n",
+      "Filters=64: 0.9067\n",
+      "============================================================\n",
+      "\n",
+      "Best: 64 filters with 0.9067 accuracy\n"
+     ]
+    }
+   ],
    "source": [
-    "# A. Test Hyperparameters"
+    "\n",
+    "# A. Test Hyperparameters - Number of Filters\n",
+    "\n",
+    "\n",
+    "from keras.layers import Conv2D, MaxPooling2D, Flatten, Dense\n",
+    "from keras.models import Sequential\n",
+    "\n",
+    "# Values to test\n",
+    "filter_options = [16, 32, 64]\n",
+    "results = []\n",
+    "\n",
+    "for filters in filter_options:\n",
+    "    print(f\"\\n🔍 Testing with {filters} filters...\")\n",
+    "    print(\"-\" * 30)\n",
+    "    \n",
+    "    # Create new model\n",
+    "    model = Sequential([\n",
+    "        Conv2D(filters, (3, 3), activation='relu', input_shape=(28, 28, 1)),\n",
+    "        MaxPooling2D((2, 2)),\n",
+    "        Conv2D(filters*2, (3, 3), activation='relu'),\n",
+    "        MaxPooling2D((2, 2)),\n",
+    "        Flatten(),\n",
+    "        Dense(64, activation='relu'),\n",
+    "        Dense(10, activation='softmax')\n",
+    "    ])\n",
+    "    \n",
+    "    model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])\n",
+    "    \n",
+    "    # Train\n",
+    "    model.fit(X_train, y_train, epochs=5, batch_size=32, validation_split=0.2, verbose=0)\n",
+    "    \n",
+    "    # Evaluate\n",
+    "    test_loss, test_acc = model.evaluate(X_test, y_test, verbose=0)\n",
+    "    print(f\"  Test accuracy: {test_acc:.4f}\")\n",
+    "    \n",
+    "    results.append({'filters': filters, 'accuracy': test_acc})\n",
+    "\n",
+    "# Results\n",
+    "print(\"\\n\" + \"=\" * 60)\n",
+    "print(\"RESULTS\")\n",
+    "print(\"=\" * 60)\n",
+    "for r in results:\n",
+    "    print(f\"Filters={r['filters']}: {r['accuracy']:.4f}\")\n",
+    "print(\"=\" * 60)\n",
+    "best = max(results, key=lambda x: x['accuracy'])\n",
+    "print(f\"\\nBest: {best['filters']} filters with {best['accuracy']:.4f} accuracy\")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 15,
    "id": "dc43ac81",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Model without dropout:\n",
+      "  Test accuracy: 0.8988\n",
+      "\n",
+      "Model with dropout:\n",
+      "  Test accuracy: 0.8886\n",
+      "\n",
+      "==================================================\n",
+      "Without dropout: 0.8988\n",
+      "With dropout:    0.8886\n",
+      "==================================================\n"
+     ]
+    }
+   ],
    "source": [
-    "# B. Test presence or absence of regularization"
+    "# B. Test presence or absence of regularization\n",
+    "from keras.layers import Dropout\n",
+    "\n",
+    "# Use best filter count from above\n",
+    "best_filters = 64\n",
+    "\n",
+    "# Model without dropout\n",
+    "print(\"\\nModel without dropout:\")\n",
+    "model1 = Sequential([\n",
+    "    Conv2D(best_filters, (3,3), activation='relu', input_shape=(28,28,1)),\n",
+    "    MaxPooling2D((2,2)),\n",
+    "    Conv2D(best_filters*2, (3,3), activation='relu'),\n",
+    "    MaxPooling2D((2,2)),\n",
+    "    Flatten(),\n",
+    "    Dense(64, activation='relu'),\n",
+    "    Dense(10, activation='softmax')\n",
+    "])\n",
+    "\n",
+    "model1.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])\n",
+    "model1.fit(X_train, y_train, epochs=5, batch_size=32, validation_split=0.2, verbose=0)\n",
+    "acc1 = model1.evaluate(X_test, y_test, verbose=0)[1]\n",
+    "print(f\"  Test accuracy: {acc1:.4f}\")\n",
+    "\n",
+    "# Model with dropout\n",
+    "print(\"\\nModel with dropout:\")\n",
+    "model2 = Sequential([\n",
+    "    Conv2D(best_filters, (3,3), activation='relu', input_shape=(28,28,1)),\n",
+    "    MaxPooling2D((2,2)),\n",
+    "    Dropout(0.25),\n",
+    "    Conv2D(best_filters*2, (3,3), activation='relu'),\n",
+    "    MaxPooling2D((2,2)),\n",
+    "    Dropout(0.25),\n",
+    "    Flatten(),\n",
+    "    Dense(64, activation='relu'),\n",
+    "    Dropout(0.5),\n",
+    "    Dense(10, activation='softmax')\n",
+    "])\n",
+    "\n",
+    "model2.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])\n",
+    "model2.fit(X_train, y_train, epochs=5, batch_size=32, validation_split=0.2, verbose=0)\n",
+    "acc2 = model2.evaluate(X_test, y_test, verbose=0)[1]\n",
+    "print(f\"  Test accuracy: {acc2:.4f}\")\n",
+    "\n",
+    "print(\"\\n\" + \"=\" * 50)\n",
+    "print(f\"Without dropout: {acc1:.4f}\")\n",
+    "print(f\"With dropout:    {acc2:.4f}\")\n",
+    "print(\"=\" * 50)"
    ]
   },
   {
@@ -226,7 +771,19 @@
    "source": [
     "Reflection: Report on the performance of the models you tested. Did any of the changes you made improve the model's performance? If so, which ones? What do you think contributed to these improvements? Finally, what combination of hyperparameters and regularization techniques yielded the best performance?\n",
     "\n",
-    "**Your answer here**"
+    "**Your answer here**\n",
+    "The filter count test showed improvement as filters increased:\n",
+    "- 16 filters: 88.9%\n",
+    "- 32 filters: 90.4%  \n",
+    "- 64 filters: 90.7%\n",
+    "\n",
+    "More filters = more patterns the network can learn, so far 64 filters worked best.  I would try another two more, but the improvement has slowed down, probably best is 91%\n",
+    "\n",
+    "The dropout test was interesting.  Without dropout got 89.9%, with dropout got 88.9%. \n",
+    "Dropout actually hurt performance here.  I assume probably because the model isn't overfitting \n",
+    "much and dropout just makes it harder to learn.\n",
+    "\n",
+    "Best combination: 64 filters, no dropout, 90.7% test accuracy."
    ]
   },
   {
@@ -244,11 +801,246 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 16,
    "id": "31f926d1",
    "metadata": {},
-   "outputs": [],
-   "source": []
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "============================================================\n",
+      "TRAINING FINAL MODEL WITH BEST SETTINGS\n",
+      "============================================================\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"sequential_11\"</span>\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[1mModel: \"sequential_11\"\u001b[0m\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
+       "┃<span style=\"font-weight: bold\"> Layer (type)                    </span>┃<span style=\"font-weight: bold\"> Output Shape           </span>┃<span style=\"font-weight: bold\">       Param # </span>┃\n",
+       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
+       "│ conv2d_12 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)              │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">26</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">26</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)     │           <span style=\"color: #00af00; text-decoration-color: #00af00\">640</span> │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ max_pooling2d_12 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>) │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">13</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">13</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)     │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ conv2d_13 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)              │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">11</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">11</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)    │        <span style=\"color: #00af00; text-decoration-color: #00af00\">73,856</span> │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ max_pooling2d_13 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>) │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">5</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">5</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)      │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ flatten_8 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Flatten</span>)             │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">3200</span>)           │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ dense_14 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)             │       <span style=\"color: #00af00; text-decoration-color: #00af00\">204,864</span> │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ dense_15 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">10</span>)             │           <span style=\"color: #00af00; text-decoration-color: #00af00\">650</span> │\n",
+       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
+       "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                   \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape          \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m      Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
+       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
+       "│ conv2d_12 (\u001b[38;5;33mConv2D\u001b[0m)              │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m26\u001b[0m, \u001b[38;5;34m26\u001b[0m, \u001b[38;5;34m64\u001b[0m)     │           \u001b[38;5;34m640\u001b[0m │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ max_pooling2d_12 (\u001b[38;5;33mMaxPooling2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m13\u001b[0m, \u001b[38;5;34m13\u001b[0m, \u001b[38;5;34m64\u001b[0m)     │             \u001b[38;5;34m0\u001b[0m │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ conv2d_13 (\u001b[38;5;33mConv2D\u001b[0m)              │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m11\u001b[0m, \u001b[38;5;34m11\u001b[0m, \u001b[38;5;34m128\u001b[0m)    │        \u001b[38;5;34m73,856\u001b[0m │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ max_pooling2d_13 (\u001b[38;5;33mMaxPooling2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m128\u001b[0m)      │             \u001b[38;5;34m0\u001b[0m │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ flatten_8 (\u001b[38;5;33mFlatten\u001b[0m)             │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m3200\u001b[0m)           │             \u001b[38;5;34m0\u001b[0m │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ dense_14 (\u001b[38;5;33mDense\u001b[0m)                │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m)             │       \u001b[38;5;34m204,864\u001b[0m │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ dense_15 (\u001b[38;5;33mDense\u001b[0m)                │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m10\u001b[0m)             │           \u001b[38;5;34m650\u001b[0m │\n",
+       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">280,010</span> (1.07 MB)\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m280,010\u001b[0m (1.07 MB)\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">280,010</span> (1.07 MB)\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m280,010\u001b[0m (1.07 MB)\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Training final model...\n",
+      "Epoch 1/20\n",
+      "\u001b[1m1500/1500\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m32s\u001b[0m 21ms/step - accuracy: 0.8306 - loss: 0.4660 - val_accuracy: 0.8723 - val_loss: 0.3587\n",
+      "Epoch 2/20\n",
+      "\u001b[1m1500/1500\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m31s\u001b[0m 20ms/step - accuracy: 0.8858 - loss: 0.3098 - val_accuracy: 0.8969 - val_loss: 0.2823\n",
+      "Epoch 3/20\n",
+      "\u001b[1m1500/1500\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m30s\u001b[0m 20ms/step - accuracy: 0.9043 - loss: 0.2614 - val_accuracy: 0.9002 - val_loss: 0.2762\n",
+      "Epoch 4/20\n",
+      "\u001b[1m1500/1500\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m30s\u001b[0m 20ms/step - accuracy: 0.9169 - loss: 0.2288 - val_accuracy: 0.9071 - val_loss: 0.2535\n",
+      "Epoch 5/20\n",
+      "\u001b[1m1500/1500\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m42s\u001b[0m 21ms/step - accuracy: 0.9273 - loss: 0.1963 - val_accuracy: 0.9087 - val_loss: 0.2564\n",
+      "Epoch 6/20\n",
+      "\u001b[1m1500/1500\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m30s\u001b[0m 20ms/step - accuracy: 0.9345 - loss: 0.1743 - val_accuracy: 0.9087 - val_loss: 0.2585\n",
+      "Epoch 7/20\n",
+      "\u001b[1m1500/1500\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m30s\u001b[0m 20ms/step - accuracy: 0.9436 - loss: 0.1518 - val_accuracy: 0.9060 - val_loss: 0.2721\n",
+      "Epoch 7: early stopping\n",
+      "Restoring model weights from the end of the best epoch: 4.\n",
+      "\n",
+      "============================================================\n",
+      "Test loss: 0.2680\n",
+      "Test accuracy: 0.9013\n",
+      "Test accuracy: 90.13%\n",
+      "============================================================\n",
+      "============================================================\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 1200x400 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# 5. Training Final Model and Evaluation\n",
+    "\n",
+    "from keras.layers import Conv2D, MaxPooling2D, Flatten, Dense\n",
+    "from keras.models import Sequential\n",
+    "from keras.callbacks import EarlyStopping\n",
+    "\n",
+    "# Best settings from experiments:\n",
+    "# - 64 filters in first conv layer\n",
+    "# - No dropout (performed better)\n",
+    "# - 128 filters in second conv layer (filters*2)\n",
+    "\n",
+    "final_model = Sequential([\n",
+    "    Conv2D(64, (3, 3), activation='relu', input_shape=(28, 28, 1)),\n",
+    "    MaxPooling2D((2, 2)),\n",
+    "    \n",
+    "    Conv2D(128, (3, 3), activation='relu'),\n",
+    "    MaxPooling2D((2, 2)),\n",
+    "    \n",
+    "    Flatten(),\n",
+    "    Dense(64, activation='relu'),\n",
+    "    Dense(10, activation='softmax')\n",
+    "])\n",
+    "\n",
+    "# Compile the model\n",
+    "final_model.compile(\n",
+    "    optimizer='adam',\n",
+    "    loss='categorical_crossentropy',\n",
+    "    metrics=['accuracy']\n",
+    ")\n",
+    "\n",
+    "# Show model summary\n",
+    "final_model.summary()\n",
+    "\n",
+    "# Set up early stopping to prevent overfitting\n",
+    "early_stop = EarlyStopping(\n",
+    "    monitor='val_loss',\n",
+    "    patience=3,\n",
+    "    restore_best_weights=True,\n",
+    "    verbose=1\n",
+    ")\n",
+    "\n",
+    "# Train the final model\n",
+    "print(\"\\nTraining final model...\")\n",
+    "history = final_model.fit(\n",
+    "    X_train, y_train,\n",
+    "    epochs=20,\n",
+    "    batch_size=32,\n",
+    "    validation_split=0.2,\n",
+    "    callbacks=[early_stop],\n",
+    "    verbose=1\n",
+    ")\n",
+    "\n",
+    "# Evaluate on test set\n",
+    "test_loss, test_acc = final_model.evaluate(X_test, y_test, verbose=0)\n",
+    "\n",
+    "print(\"\\n\" + \"=\" * 60)\n",
+    "print(f\"Test loss: {test_loss:.4f}\")\n",
+    "print(f\"Test accuracy: {test_acc:.4f}\")\n",
+    "print(f\"Test accuracy: {test_acc*100:.2f}%\")\n",
+    "print(\"=\" * 60)\n",
+    "print(\"=\" * 60)\n",
+    "# Plot training history\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "plt.figure(figsize=(12, 4))\n",
+    "\n",
+    "plt.subplot(1, 2, 1)\n",
+    "plt.plot(history.history['accuracy'], label='Training')\n",
+    "plt.plot(history.history['val_accuracy'], label='Validation')\n",
+    "plt.title('Model Accuracy')\n",
+    "plt.xlabel('Epoch')\n",
+    "plt.ylabel('Accuracy')\n",
+    "plt.legend()\n",
+    "plt.grid(True, alpha=0.3)\n",
+    "\n",
+    "plt.subplot(1, 2, 2)\n",
+    "plt.plot(history.history['loss'], label='Training')\n",
+    "plt.plot(history.history['val_loss'], label='Validation')\n",
+    "plt.title('Model Loss')\n",
+    "plt.xlabel('Epoch')\n",
+    "plt.ylabel('Loss')\n",
+    "plt.legend()\n",
+    "plt.grid(True, alpha=0.3)\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
   },
   {
    "cell_type": "markdown",
@@ -257,7 +1049,22 @@
    "source": [
     "Reflection: How does the final model's performance compare to the baseline and the CNN model? What do you think contributed to the final model's performance? If you had time, what other experiments would you run to further improve the model's performance?\n",
     "\n",
-    "**Your answer here**"
+    "**Your answer here**\n",
+    "The final model hit 90.1% test accuracy, which beats Baseline model: 83.0% (+7.1%) but is about the same of the CNN from earlier: 90.7% (about the same)\n",
+    "\n",
+    "The 64 filters (vs 32) helped capture more patterns, and skipping dropout \n",
+    "let the model learn without restrictions. The early stopping kept the best \n",
+    "weights from epoch 4 (90.7% validation) before overfitting started.\n",
+    "\n",
+    "If I had more time, I'd try:\n",
+    "- Data augmentation (rotate/zoom images to create more training data)\n",
+    "- Batch normalization to speed up training (it took a while!)\n",
+    "- More filters (96 or 128 in first layer, alhtough not sure more filters will improve much more)\n",
+    "- Different optimizers like SGD with momentum\n",
+    "\n",
+    "\n",
+    "For the graph, it shows that epch 3 was the sweet spot...after that training loss keeps dropping..so it is overfitting and the model is memorizing the times table after that.  The early stop says epoch 4...The diffference is negligable for this task, but it is interesting that the early stop and the graph disagree.\n",
+    "\n"
    ]
   },
   {
@@ -283,13 +1090,35 @@
     "- [ ] Verify that the link is accessible in a private browser window.\n",
     "If you encounter any difficulties or have questions, please don't hesitate to reach out to our team via our Slack at `#cohort-7-help-ml`. Our Technical Facilitators and Learning Support staff are here to help you navigate any challenges."
    ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "20fd9ee8",
+   "metadata": {},
+   "source": [
+    "The final model hit 90.1% test accuracy, which beats:\n",
+    "- Baseline model: 83.0% (+7.1%)\n",
+    "- Simple CNN from earlier: 90.0% (about the same)\n",
+    "\n",
+    "The 64 filters (vs 32) helped capture more patterns, and skipping dropout \n",
+    "let the model learn without restrictions. The early stopping kept the best \n",
+    "weights from epoch 4 (90.7% validation) before overfitting started.\n",
+    "\n",
+    "If I had more time, I'd try:\n",
+    "- Data augmentation (rotate/zoom images to create more training data)\n",
+    "- Batch normalization to speed up training\n",
+    "- More filters (96 or 128 in first layer)\n",
+    "- Different optimizers like SGD with momentum\n",
+    "\n",
+    "But 90% is solid for Fashion MNIST - the dataset has a ceiling around 93-94%."
+   ]
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "deep_learning",
+   "display_name": "Deep Learning (.venv)",
    "language": "python",
-   "name": "python3"
+   "name": "deeplearning_venv"
   },
   "language_info": {
    "codemirror_mode": {
@@ -301,7 +1130,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.12.11"
+   "version": "3.11.0rc1"
   }
  },
  "nbformat": 4,