With continuously evolving research and development, Generative Adversarial Networks (GANs) are the next big thing in the field of deep learning. This book highlights the key improvements in GANs over traditional generative models and shows you how to make the best out of GANs with the help of hands-on examples.
This book will help you understand how GAN architecture works using PyTorch. You will get familiar with the most flexible deep learning toolkit and use it to transform ideas into actual working code. You will apply GAN models to areas such as computer vision, multimedia, and natural language processing using a sample-generation methodology.
