Generative Deep Learning by Foster, David -- Read -- Imperial Library of Trantor

Index

Preface

Objective and Approach Prerequisites Other Resources Conventions Used in This Book Using Code Examples O’Reilly Online Learning How to Contact Us Acknowledgments

I. Introduction to Generative Deep Learning 1. Generative Modeling

What Is Generative Modeling?

Generative Versus Discriminative Modeling Advances in Machine Learning The Rise of Generative Modeling The Generative Modeling Framework

Probabilistic Generative Models

Hello Wrodl! Your First Probabilistic Generative Model Naive Bayes Hello Wrodl! Continued

The Challenges of Generative Modeling

Representation Learning

Setting Up Your Environment Summary

2. Deep Learning

Structured and Unstructured Data Deep Neural Networks

Keras and TensorFlow

Your First Deep Neural Network

Loading the Data Building the Model Compiling the Model Training the Model Evaluating the Model

Improving the Model

Convolutional Layers Batch Normalization Dropout Layers Putting It All Together

Summary

3. Variational Autoencoders

The Art Exhibition Autoencoders

Your First Autoencoder The Encoder The Decoder Joining the Encoder to the Decoder Analysis of the Autoencoder

The Variational Art Exhibition Building a Variational Autoencoder

The Encoder The Loss Function Analysis of the Variational Autoencoder

Using VAEs to Generate Faces

Training the VAE Analysis of the VAE Generating New Faces Latent Space Arithmetic Morphing Between Faces

Summary

4. Generative Adversarial Networks

Ganimals Introduction to GANs Your First GAN

The Discriminator The Generator Training the GAN

GAN Challenges

Oscillating Loss Mode Collapse Uninformative Loss Hyperparameters Tackling the GAN Challenges

Wasserstein GAN

Wasserstein Loss The Lipschitz Constraint Weight Clipping Training the WGAN Analysis of the WGAN

WGAN-GP

The Gradient Penalty Loss Analysis of WGAN-GP

Summary

II. Teaching Machines to Paint, Write, Compose, and Play 5. Paint

Apples and Organges CycleGAN Your First CycleGAN

Overview The Generators (U-Net) The Discriminators Compiling the CycleGAN Training the CycleGAN Analysis of the CycleGAN

Creating a CycleGAN to Paint Like Monet

The Generators (ResNet) Analysis of the CycleGAN

Neural Style Transfer

Content Loss Style Loss Total Variance Loss Running the Neural Style Transfer Analysis of the Neural Style Transfer Model

Summary

6. Write

The Literary Society for Troublesome Miscreants Long Short-Term Memory Networks Your First LSTM Network

Tokenization Building the Dataset The LSTM Architecture The Embedding Layer The LSTM Layer The LSTM Cell

Generating New Text RNN Extensions

Stacked Recurrent Networks Gated Recurrent Units Bidirectional Cells

Encoder–Decoder Models A Question and Answer Generator

A Question-Answer Dataset Model Architecture Inference Model Results

Summary

7. Compose

Preliminaries

Musical Notation

Your First Music-Generating RNN

Attention Building an Attention Mechanism in Keras Analysis of the RNN with Attention Attention in Encoder–Decoder Networks Generating Polyphonic Music

The Musical Organ Your First MuseGAN The MuseGAN Generator

Chords, Style, Melody, and Groove

Chords Style Melody Groove

The Bar Generator Putting It All Together

The Critic Analysis of the MuseGAN Summary

8. Play

Reinforcement Learning

OpenAI Gym

World Model Architecture

The Variational Autoencoder The MDN-RNN The Controller

Setup Training Process Overview Collecting Random Rollout Data Training the VAE

The VAE Architecture Exploring the VAE

The full model The encoder models The decoder model

Collecting Data to Train the RNN Training the MDN-RNN

The MDN-RNN Architecture Sampling the Next z and Reward from the MDN-RNN The MDN-RNN Loss Function

Training the Controller

The Controller Architecture CMA-ES Parallelizing CMA-ES Output from the Controller Training

In-Dream Training

In-Dream Training the Controller Challenges of In-Dream Training

Summary

9. The Future of Generative Modeling

Five Years of Progress The Transformer

Positional Encoding Multihead Attention The Decoder Analysis of the Transformer BERT GPT-2 MuseNet

Advances in Image Generation

ProGAN Self-Attention GAN (SAGAN) BigGAN StyleGAN

Applications of Generative Modeling

AI Art AI Music

10. Conclusion Index

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