Fix typos

Author: havk64

02_TensorFlow_Way/readme.md

@@ -8,9 +8,9 @@ After we have established the basic objects and methods in TensorFlow, we now wa
   * We show how to create multiple operations on a computational graph and how to visualize them using Tensorboard.
  3. [Working with Multiple Layers](03_Working_with_Multiple_Layers)
   * Here we extend the usage of the computational graph to create multiple layers and show how they appear in Tensorboard.
- 4. [Implmenting Loss Functions](04_Implementing_Loss_Functions)
+ 4. [Implementing Loss Functions](04_Implementing_Loss_Functions)
   * In order to train a model, we must be able to evaluate how well it is doing. This is given by loss functions. We plot various loss functions and talk about the benefits and limitations of some.
- 5. [Implmenting Back Propagation](05_Implementing_Back_Propagation)
+ 5. [Implementing Back Propagation](05_Implementing_Back_Propagation)
   * Here we show how to use loss functions to iterate through data and back propagate errors for regression and classification.
  6. [Working with Stochastic and Batch Training](06_Working_with_Batch_and_Stochastic_Training)
   * TensorFlow makes it easy to use both batch and stochastic training. We show how to implement both and talk about the benefits and limitations of each.

03_Linear_Regression/readme.md

@@ -11,10 +11,10 @@ Here we show how to implement various linear regression techniques in TensorFlow
  4. [Understanding Loss Functions in Linear Regression](04_Loss_Functions_in_Linear_Regressions)
   * L2 vs L1 loss in linear regression.  We talk about the benefits and limitations of both.
  5. [Implementing Deming Regression (Total Regression)](05_Implementing_Deming_Regression)
-  * Deming (total) regression implmented in TensorFlow by changing the loss function.
+  * Deming (total) regression implemented in TensorFlow by changing the loss function.
  6. [Implementing Lasso and Ridge Regression](06_Implementing_Lasso_and_Ridge_Regression)
   * Lasso and Ridge regression are ways of regularizing the coefficients. We implement both of these in TensorFlow via changing the loss functions.
  7. [Implementing Elastic Net Regression](07_Implementing_Elasticnet_Regression)
   * Elastic net is a regularization technique that combines the L2 and L1 loss for coefficients.  We show how to implement this in TensorFlow.
  8. [Implementing Logistic Regression](08_Implementing_Logistic_Regression)
-  * We implment logistic regression by the use of an activation function in our computational graph.
+  * We implement logistic regression by the use of an activation function in our computational graph.

04_Support_Vector_Machines/02_Working_with_Linear_SVMs/readme.md

@@ -1,6 +1,6 @@
 # Working with Linear SVMs
 
-We introduce a linear SVM on a binary set, which will be a subset of the Iris data.  We know for I. setosa, that petal width and sepal length are completely seperable. We will create a linear SVM to predict I. setosa based on two features: petal width and sepal length.
+We introduce a linear SVM on a binary set, which will be a subset of the Iris data.  We know for I. setosa, that petal width and sepal length are completely separable. We will create a linear SVM to predict I. setosa based on two features: petal width and sepal length.
 
 It is worth noting that due to the small data set and the randomness of separating into train/test sets, that it may appear that a few points can end up on the wrong side of the line.  This is because they are in the test set, and this will result in a lower test accuracy.
 

04_Support_Vector_Machines/readme.md

@@ -10,7 +10,7 @@ This chapter shows how to implement various SVM methods with TensorFlow.  We fir
   * The heart of SVMs is separating classes with a line.  We change tweek the algorithm slightly to perform SVM regression.
  4. [Working with Kernels in TensorFlow](04_Working_with_Kernels)
   * In order to extend SVMs into non-linear data, we explain and show how to implement different kernels in TensorFlow.
- 5. [Implmenting Non-Linear SVMs](05_Implementing_Nonlinear_SVMs)
+ 5. [Implementing Non-Linear SVMs](05_Implementing_Nonlinear_SVMs)
   * We use the Gaussian kernel (RBF) to separate non-linear classes.
  6. [Implementing Multi-class SVMs](06_Implementing_Multiclass_SVMs)
   * SVMs are inherently binary predictors.  We show how to extend them in a one-vs-all strategy in TensorFlow.

06_Neural_Networks/08_Learning_Tic_Tac_Toe/tic_tac_toe_moves.py

@@ -83,7 +83,7 @@ def get_symmetry(board, response, transformation):
         new_board = board[::-1]
         return(new_board[6:9] +  new_board[3:6] + new_board[0:3], new_response)
     else:
-        raise ValueError('Method not implmented.')
+        raise ValueError('Method not implemented.')
 
 
 ## Read in board move csv file

06_Neural_Networks/readme.md

@@ -8,7 +8,7 @@ Neural Networks are very important in machine learning and growing in popularity
   * We implement an operational gate with one operation. Then we show how to extend this to multiple nested operations.
  3. [Working with Gates and Activation Functions](03_Working_with_Activation_Functions)
   * Now we have to introduce activation functions on the gates.  We show how different activation functions operate.
- 4. [Implmenting a One Layer Neural Network](04_Single_Hidden_Layer_Network)
+ 4. [Implementing a One Layer Neural Network](04_Single_Hidden_Layer_Network)
   * We have all the pieces to start implementing our first neural network.  We do so here with regression on the Iris data set.
  5. [Implementing Different Layers](05_Implementing_Different_Layers)
   * This section introduces the convolution layer and the max-pool layer.  We show how to chain these together in a 1D and 2D example with fully connected layers as well.

07_Natural_Language_Processing/04_Working_With_Skip_Gram_Embeddings/04_working_with_skipgram.py

@@ -201,7 +201,7 @@ def generate_batch_data(sentences, batch_size, window_size, method='skip_gram'):
             # Make it in to a big list of tuples (target word, surrounding word)
             tuple_data = [(x_, y) for x,y in batch_and_labels for x_ in x]
         else:
-            raise ValueError('Method {} not implmented yet.'.format(method))
+            raise ValueError('Method {} not implemented yet.'.format(method))
 
         # extract batch and labels
         batch, labels = [list(x) for x in zip(*tuple_data)]

07_Natural_Language_Processing/05_Working_With_CBOW_Embeddings/text_helpers.py

@@ -102,7 +102,7 @@ def generate_batch_data(sentences, batch_size, window_size, method='skip_gram'):
             # Add document index to batch!! Remember that we must extract the last index in batch for the doc-index
             batch = [x + [rand_sentence_ix] for x in batch]
         else:
-            raise ValueError('Method {} not implmented yet.'.format(method))
+            raise ValueError('Method {} not implemented yet.'.format(method))
 
         # extract batch and labels
         batch_data.extend(batch[:batch_size])

07_Natural_Language_Processing/06_Using_Word2Vec_Embeddings/text_helpers.py

@@ -102,7 +102,7 @@ def generate_batch_data(sentences, batch_size, window_size, method='skip_gram'):
             # Add document index to batch!! Remember that we must extract the last index in batch for the doc-index
             batch = [x + [rand_sentence_ix] for x in batch]
         else:
-            raise ValueError('Method {} not implmented yet.'.format(method))
+            raise ValueError('Method {} not implemented yet.'.format(method))
 
         # extract batch and labels
         batch_data.extend(batch[:batch_size])

07_Natural_Language_Processing/07_Sentiment_Analysis_With_Doc2Vec/text_helpers.py

@@ -102,7 +102,7 @@ def generate_batch_data(sentences, batch_size, window_size, method='skip_gram'):
             # Add document index to batch!! Remember that we must extract the last index in batch for the doc-index
             batch = [x + [rand_sentence_ix] for x in batch]
         else:
-            raise ValueError('Method {} not implmented yet.'.format(method))
+            raise ValueError('Method {} not implemented yet.'.format(method))
 
         # extract batch and labels
         batch_data.extend(batch[:batch_size])