Generative Deep Learning: Teaching Machines to Paint, Write, Compose, and Play
O'Reilly Media | English | 2019 | ISBN-10: 1492041947 | 330 pages | PDF | 29.19 MB
by David Foster (Author)
This book covers the key techniques that have dominated the generative modeling landscape in recent years and have allowed us to make impressive progress in creative tasks. As well as covering core generative modeling theory, we will be building full working examples of some of the key models from the literature and walking through the codebase for each, step by step.
Throughout the book, you will find short, allegorical stories that help explain the mechanics of some of the models we will be building. I believe that one of the best ways to teach a new abstract theory is to first convert it into something that isn’t quite so abstract, such as a story, before diving into the technical explanation. The individual steps of the theory are clearer within this context because they involve people, actions, and emotions, all of which are well understood, rather than abstract constructs such as neural networks, backpropagation, and loss functions.
The story and the model explanation are just the same mechanics explained in two different domains. You might therefore find it useful to refer back to the relevant story while learning about each model. If you are already familiar with a particular technique, then have fun finding the parallels of each model element within the story!
In Part I of this book I shall introduce the key techniques that we will be using to build generative models, including an overview of deep learning, variational autoencoders, and generative adversarial networks