Generative Deep Learning

Part I. Introduction to Generative Deep Learning

The first four chapters of this book aim to introduce the core techniques that you’ll need to start building generative deep learning models.

In Chapter 1, we shall first take a broad look at the field of generative modeling and consider the type of problem that we are trying to solve from a probabilistic perspective. We will then explore our first example of a basic probabilistic generative model and analyze why deep learning techniques may need to be deployed as the complexity of the generative task grows.

Chapter 2 provides a guide to the deep learning tools and techniques that you will need to start building more complex generative models. This is intended to be a practical guide to deep learning rather than a theoretical analysis of the field. In particular, I will introduce Keras, a framework for building neural networks that can be used to construct and train some of the most cutting-edge deep neural network architectures published in the literature.

In Chapter 3, we shall take a look at our first generative deep learning model, the variational autoencoder. This powerful technique will allow us to not only generate realistic faces, but also alter existing images—for example, by adding a smile or changing the color of someone’s hair.

Chapter 4 explores one of the most successful generative modeling techniques of recent years, the generative adversarial network. This elegant framework for structuring a generative modeling ...