Deep Learning with Python

Chapter I 1. Getting Started with Deep Learning 1 1.1. Introducing machine learning 1 1.2. What is deep learning 4 1.3. How the human brain works 4 1.4. Deep learning history 6 1.5. Neural networks 6 1.6. The biological neuron 6 1.7. An artificial neuron 7 1.8. The backpropagation algorithm 9 1.9. Weights optimization 10 1.10. Stochastic gradient descent 11 1.11. Neural network architectures 12 1.12. Multilayer perceptron 12 1.13. DNNs architectures 13 1.14. Convolutional Neural Networks 13 1.15. Restricted Boltzmann Machines 14 1.16. Autoencoders 15 1.17. Recurrent Neural Networks 16 1.18. Deep learning framework comparisons 17



Chapter II 2. Feed-Forward Neural Network 19 2.1. Introducing feed-forward neural network 19 2.2. Classification of handwritten digits 22 2.3. How to save and restore a TensorFlow model 34 2.4. Implementing a five-layer neural network 37 2.5. Dropout optimization 43

Chapter III 3. Convolutional Neural Network 48 3.1. Introducing CNN networks 48 3.2. CNN architecture 51 3.3. A model for CNNs - LeNet 53 3.4. Building your first CNN 54 3.5. Emotion recognition with CNNs 65

Chapter IV 4. Optimizing TensorFlow Autoencoders 81 4.1. Introducing autoencoders 82 4.2. Implementing an autoencoder 82 4.3. Improving autoencoder robustness 89 4.4. Building denoising autoencoders 90

Chapter V 5. Recurrent Neural Networks 99 5.1. RNN basic concepts 99 5.2. RNNs at work 101 5.3. Unfolding an RNN 101 5.4. The vanishing gradient problem 102 5.5. LSTM networks 104 5.6. An image classifier with RNNs 105 5.7. Bidirectional RNNs 112 5.8. Text processing 120

Chapter VI 6. Advanced TensorFlow Programming 125 6.1. Introducing Keras 125 6.2. Building deep learning models 127 6.3. Sentiment classification of movie reviews 129 6.4. Adding a convolutional layer 134 6.5. Pretty Tensor 135 6.6. Digit classifier 136

Chapter VII 7. Reinforcement Learning 142 7.1. Basic concepts of Reinforcement Learning 142 7.2. Q-learning algorithm 145 7.3. Introducing the OpenAI Gym framework 147 7.4. Implementing FrozenLake-v0 149 7.5. Q-learning with TensorFlow 152