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 s 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 working on ML tooling and was a Computer Vision Lead at Founders Factory.

Vladimir Bok

is a Senior Product Manager overseeing machine learning infrastructure and research teams at a New York-based startup.

Table of Contents

PART 1 - INTRODUCTION TO GANS AND GENERATIVE MODELING

Introduction to GANs

Intro to generative modeling with autoencoders

Your first GAN: Generating handwritten digits

Deep Convolutional GAN

PART 2 - ADVANCED TOPICS IN GANS

Training and common challenges: GANing for success

Progressing with GANs

Semi-Supervised GAN

Conditional GAN

CycleGANPART 3 - WHERE TO GO FROM HERE

Adversarial examples

Practical applications of GANs

Looking ahead

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