From the Book - First edition.
Part 1. Introduction to generative deep learning. Generative modeling
Generative adversarial networks
Part 2. Teaching machines to paint, write, compose, and play. Paint
The future of generative modeling
From the eBook - Second edition.
Part I. Introduction to Generative Deep Learning
Chapter 1. Generative Modeling
What Is Generative Modeling?
Generative Versus Discriminative Modeling
The Rise of Generative Modeling
Generative Modeling and AI
Our First Generative Model
The Generative Modeling Framework
Generative Model Taxonomy
The Generative Deep Learning Codebase
What Is a Neural Network?
Learning High-Level Features
Multilayer Perceptron (MLP)
Convolutional Neural Network (CNN)
Training and Evaluating the CNN
Chapter 3. Variational Autoencoders
The Fashion-MNIST Dataset
The Autoencoder Architecture
Joining the Encoder to the Decoder
Visualizing the Latent Space
Training the Variational Autoencoder
Analysis of the Variational Autoencoder
Exploring the Latent Space
Training the Variational Autoencoder
Analysis of the Variational Autoencoder
Chapter 4. Generative Adversarial Networks
Deep Convolutional GAN (DCGAN)
GAN Training: Tips and Tricks
Wasserstein GAN with Gradient Penalty (WGAN-GP)
Enforcing the Lipschitz Constraint
The Gradient Penalty Loss
Chapter 5. Autoregressive Models
Long Short-Term Memory Network (LSTM)
Creating the Training Set
Recurrent Neural Network (RNN) Extensions
Stacked Recurrent Networks
