Video description
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 audience
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.
An incredibly useful mix of practical and academic information.
Dana Robinson, The HDF Group
A great systematization of the rapidly evolving and vast GAN landscape.
Grigory V. Sapunov, Intento
Excellent writing combined with easy-to-grasp mathematical explanations.
Bachir Chihani, C3
Strikes that rare balance between an applied programming book, an academic book heavy on theory, and a conversational blog post on machine learning techniques.
Dr. Erik Sapper, California Polytechnic State University
NARRATED BY JULIE BRIERLEY
Table of Contents
Part 1. Introduction to GANs and generative modeling
Chapter 1. Introduction to GANs
Chapter 1. GANs in action
Chapter 1. Why study GANs?
Chapter 2. Intro to generative modeling with autoencoders
Chapter 2. What are autoencoders to GANs?
Chapter 2. Unsupervised learning
Chapter 2. Code is life
Chapter 2. Why did we try aGAN?
Chapter 3. Your first GAN: Generating handwritten digits
Chapter 3. The Generator and the Discriminator
Chapter 3. Implementing the Discriminator
Chapter 4. Deep Convolutional GAN
Chapter 4. Batch normalization
Chapter 4. Implementing the Generator
Part 2. Advanced topics in GANs
Chapter 5. Training and common challenges: GANing for success
Chapter 5. Inception score
Chapter 5. Training challenges
Chapter 5. Min-Max GAN
Chapter 5. Wasserstein GAN
Chapter 5. Summary of game setups
Chapter 6. Progressing with GANs
Chapter 6. They grow up so fast
Chapter 6. Equalized learning rate
Chapter 6. Summary of key innovations
Chapter 7. Semi-Supervised GAN
Chapter 7. What is a Semi-Supervised GAN?
Chapter 7. Tutorial: Implementing a Semi-Supervised GAN
Chapter 7. Building the model
Chapter 8. Conditional GAN
Chapter 8. Tutorial: Implementing a Conditional GAN
Chapter 8. Building the model
Chapter 9. CycleGAN
Chapter 9. Architecture
Chapter 9. Object-oriented design of GANs
Chapter 9. Training the CycleGAN
Part 3. Where to go from here
Chapter 10. Adversarial examples
Chapter 10. Use and abuse of training
Chapter 10. Not all hope is lost
Chapter 11. Practical applications of GANs
Chapter 11. Methodology
Chapter 11. Creating new items matching individual preferences
Chapter 12. Looking ahead
Chapter 12. GAN innovations
Chapter 12. BigGAN