Part 1. Introduction to GANs and generative modeling

Part 1 introduces the world of Generative Adversarial Networks (GANs) and walks through implementations of the most canonical GAN variants:

• In chapter 1, you will learn the basics of GANs and develop an intuitive understanding of how they work.
• In chapter 2, we will switch gears a little and look at autoencoders, so you can get a more holistic understanding of generative modeling. Autoencoders are some of the most important theoretical and practical precursors to GANs and continue to be widely used to this day.
• Chapter 3 starts where chapter 1 left off and dives deeper into the theory underlying GANs and adversarial learning. In this chapter, you will also implement and train your first, fully functional GAN.
• Chapter 4 continues your learning journey by exploring the Deep Convolutional GAN (DCGAN). This innovation on top of the original GAN uses convolutional neural networks to improve the quality of the generated images.