GANs in Action: Deep learning with Generative Adversarial Networks

  • 6h 23m 21s
  • Jakub Langr, Vladimir Bok
  • Manning Publications
  • 2021

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

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 Audiobook

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

In this Audiobook

  • Chapter 1 - Introduction to GANs
  • Chapter 2 - Intro to Generative Modeling with Autoencoders
  • Chapter 3 - Your First GAN: Generating Handwritten Digits
  • Chapter 4 - Deep Convolutional GAN
  • Chapter 5 - Training and Common Challenges: GANing for Success
  • Chapter 6 - Progressing with GANs
  • Chapter 7 - Semi-Supervised GAN
  • Chapter 8 - Conditional GAN
  • Chapter 9 - CycleGAN
  • Chapter 10 - Adversarial Examples
  • Chapter 11 - Practical Applications of GANs
  • Chapter 12 - Looking Ahead