Developing Generative Adversarial Networks (GANs) is a complex task, and it is often hard to find code that is easy to understand. This book leads you through eight different examples of modern GAN implementation, including CycleGAN, SimGAN, DCGAN, and imitation learning with GANs. Each chapter builds on a common architecture in Python and Keras to explore increasingly difficult GAN architectures in an easy-to-read format.
The Generative Adversarial Networks Cookbook starts by covering the different types of GAN architecture to help you understand how the model works. You will learn how to perform key tasks and operations, such as creating false and high-resolution images, text-to-image synthesis, and generating videos with this recipe-based guide. You will also work with use cases such as DCGAN and deepGAN. To become well versed in the working of complex applications, you will take different real-world datasets and put them to use.
By the end of this book, you will be equipped to deal with the challenges and issues that you may face while working with GAN models thanks to easy-to-follow code solutions that you can implement right away.
