Added readme

Author: amitkaps

README.md

@@ -1,66 +1,22 @@
 # Deep Learning Bootcamp
 
-## Session 1: Deep Learning (DL) Theory
-
-1. What is deep learning?
-2. Use cases in computer vision and natural language processing.
-3. Introduction to problem and data-set - Fashion MNIST
-4. Working on the cloud, including `keras` and `tensorflow`: Rorodeep
-5. Build your ML model - t-SNE + Multi-Class Logistic Regression (sklearn)
-6. Build your first DL Model - Multi-layer Perceptron (MLP)
-
-## Session 2: Overview of the building blocks
-
-1. Input, Output & Loss Function
-2. Neurons
-3. Activation functions
-4. Back propagation algorithm
-5. Stochastic gradient descent / Adaptive learning / Momentum
-6. Training & Validation
-7. Architecture
-
-## Session 3: Convolutional Neural Networks (CNN)
-
-1. Build your second DL Model - CNN
-2. Concept of Convolution
-    - Kernel Size
-    - Shared Weights
-    - Pooling 
-    - Padding & Stride
-3. Tricks to improve your model
-   - Augment your training data
-   - Batch normalization (Kill)
-
-## Session 4: Transfer Learning
-
-1. Concept of Transfer Learning
-2. Build your third DL Model - Leverage pre-trained models
-3. Deploying your DL model on the cloud (Flask and deploy - Jinja template)
-
-## Session 5: DL for Natural Language Processing (NLP)
-
-1. Recap of Day One
-2. Challanges with traditional NLP techniques
-3. Concept of Word Embedding - word2vec
-3. Build your fourth DL Model - MLP using word2vec
-
-## Session 6: Recurrent Neural Networks (RNN)
-
-1. Concept of RNNs 
-2. Concept of Long Short-Term Memory (LSTM)
-3. Build your fifth DL Model - LSTM
+# Deep Learning Bootcamp
 
-## Session 7: Build your DL Applications
+- Introduction, Multi-layer Perceptron
+- Neural Network Building Blocks
+- Convolution Neural Network
+- Transfer Learning
+- Word Embedding
+- Recurrent Neural Network
+- Deployment
 
-1. Concept of Sequence-to-Sequence Learning
-2. Build your sixth DL Model - Text Generation
-3. Deploy it as a bot (e.g. TweetBot / ChatBot)
+## Resources
 
-## Session 8: Advanced Topics in DL (Theory)
+1. Overview Decks
+  - [For Image](https://speakerdeck.com/amitkaps/deep-learning-for-image)
+  - [For NLP](https://www.slideshare.net/amitkaps/deep-learning-for-nlp-69972908) 
 
-1. Challenges in building DL apps
-2. Concept of Generative Adversarial Network
-3. Moving beyond Classification e.g. Object Detection
-4. Concept of DL for Unsupervised Learning
-5. Concept of Reinforcement Learning
-6. Where to go from here...
+2. Articles 
+  - [Logo Detection](https://www.oreilly.com/ideas/logo-detection-using-apache-mxnet)
+  - [Uncovering Hidden Pattern](https://www.oreilly.com/ideas/uncovering-hidden-patterns-through-machine-learning)
+  - [How to learn Deep Learning in 6 months](https://towardsdatascience.com/how-to-learn-deep-learning-in-6-months-e45e40ef7d48)

Schedule.md

@@ -0,0 +1,66 @@
+# Deep Learning Bootcamp
+
+## Session 1: Deep Learning (DL) Theory
+
+1. What is deep learning?
+2. Use cases in computer vision and natural language processing.
+3. Introduction to problem and data-set - Fashion MNIST
+4. Working on the cloud, including `keras` and `tensorflow`: Rorodeep
+5. Build your ML model - t-SNE + Multi-Class Logistic Regression (sklearn)
+6. Build your first DL Model - Multi-layer Perceptron (MLP)
+
+## Session 2: Overview of the building blocks
+
+1. Input, Output & Loss Function
+2. Neurons
+3. Activation functions
+4. Back propagation algorithm
+5. Stochastic gradient descent / Adaptive learning / Momentum
+6. Training & Validation
+7. Architecture
+
+## Session 3: Convolutional Neural Networks (CNN)
+
+1. Build your second DL Model - CNN
+2. Concept of Convolution
+    - Kernel Size
+    - Shared Weights
+    - Pooling 
+    - Padding & Stride
+3. Tricks to improve your model
+   - Augment your training data
+   - Batch normalization (Kill)
+
+## Session 4: Transfer Learning
+
+1. Concept of Transfer Learning
+2. Build your third DL Model - Leverage pre-trained models
+3. Deploying your DL model on the cloud (Flask and deploy - Jinja template)
+
+## Session 5: DL for Natural Language Processing (NLP)
+
+1. Recap of Day One
+2. Challanges with traditional NLP techniques
+3. Concept of Word Embedding - word2vec
+3. Build your fourth DL Model - MLP using word2vec
+
+## Session 6: Recurrent Neural Networks (RNN)
+
+1. Concept of RNNs 
+2. Concept of Long Short-Term Memory (LSTM)
+3. Build your fifth DL Model - LSTM
+
+## Session 7: Build your DL Applications
+
+1. Concept of Sequence-to-Sequence Learning
+2. Build your sixth DL Model - Text Generation
+3. Deploy it as a bot (e.g. TweetBot / ChatBot)
+
+## Session 8: Advanced Topics in DL (Theory)
+
+1. Challenges in building DL apps
+2. Concept of Generative Adversarial Network
+3. Moving beyond Classification e.g. Object Detection
+4. Concept of DL for Unsupervised Learning
+5. Concept of Reinforcement Learning
+6. Where to go from here...