GANs in Action
Deep learning with Generative Adversarial Networks
Jakub Langr and Vladimir Bok
  • September 2019
  • ISBN 9781617295560
  • 240 pages
  • printed in black & white

Comprehensive and in-depth coverage of the future of AI.

Simeon Leyzerzon, Excelsior Software

GANs in Action teaches you how to build and train your own Generative Adversarial Networks, one of the most important innovations in deep learning. In this book, you'll learn how to start building your own simple adversarial system as you explore the foundation of GAN architecture: the generator and discriminator networks.

About the Technology

Generative Adversarial Networks, GANs, are an incredible AI technology capable of creating images, sound, and videos that are indistinguishable from the "real thing." By pitting two neural networks against each other—one to generate fakes and one to spot them—GANs rapidly learn to produce photo-realistic faces and other media objects. With the potential to produce stunningly realistic animations or shocking deepfakes, GANs are a huge step forward in deep learning systems.

About the book

GANs in Action teaches you to build and train your own Generative Adversarial Networks. You'll start by creating simple generator and discriminator networks that are the foundation of GAN architecture. Then, following numerous hands-on examples, you'll train GANs to generate high-resolution images, image-to-image translation, and targeted data generation. Along the way, you'll find pro tips for making your system smart, effective, and fast.

What's inside

  • Building your first GAN
  • Handling the progressive growing of GANs
  • Practical applications of GANs
  • Troubleshooting your system

About the reader

For data professionals with intermediate Python skills, and the basics of deep learning–based image processing.

About the author

Jakub Langr is a Computer Vision Cofounder at Founders Factory (YEPIC.AI). Vladimir Bok is a Senior Product Manager overseeing machine learning infrastructure and research teams at a New York–based startup.


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