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Generative AI with Python and TensorFlow 2

You're reading from  Generative AI with Python and TensorFlow 2

Product type Book
Published in Apr 2021
Publisher Packt
ISBN-13 9781800200883
Pages 488 pages
Edition 1st Edition
Languages
Authors (2):
Joseph Babcock Joseph Babcock
Profile icon Joseph Babcock
Raghav Bali Raghav Bali
Profile icon Raghav Bali
View More author details
Toc

Table of Contents (16) Chapters close

Preface 1. An Introduction to Generative AI: "Drawing" Data from Models 2. Setting Up a TensorFlow Lab 3. Building Blocks of Deep Neural Networks 4. Teaching Networks to Generate Digits 5. Painting Pictures with Neural Networks Using VAEs 6. Image Generation with GANs 7. Style Transfer with GANs 8. Deepfakes with GANs 9. The Rise of Methods for Text Generation 10. NLP 2.0: Using Transformers to Generate Text 11. Composing Music with Generative Models 12. Play Video Games with Generative AI: GAIL 13. Emerging Applications in Generative AI 14. Other Books You May Enjoy
15. Index

Generative adversarial networks

GANs have a pretty interesting origin story. It all began as a discussion/argument in a bar with Ian Goodfellow and friends discussing work related to generating data using neural networks. The argument ended with everyone downplaying each other's methods. Goodfellow went back home and coded the first version of what we now call a GAN. To his amazement, the code worked on the first try. A more verbose description of the chain of events was shared by Goodfellow himself in an interview with Wired magazine.

As mentioned, GANs are implicit density functions that sample directly from the underlying distribution. They do this by defining a two-player game of adversaries. The adversaries compete against each other under well-defined reward functions and each player tries to maximize its rewards. Without going into the details of game theory, the framework can be explained as follows.

The discriminator model

This model represents a differentiable...

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