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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

Table of Contents (16) Chapters

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

Deepfakes overview

Deepfakes is an all-encompassing term representing content generated using artificial intelligence (in particular, deep learning) that seems realistic and authentic to a human being. The generation of fake content or manipulation of existing content to suit the needs and agenda of the entities involved is not new. In the introduction, we discussed a few movies where CGI and painstaking manual effort helped in generating realistic results. With advancements in deep learning and, more specifically, generative models, it is becoming increasingly difficult to differentiate between what is real and what is fake.

Generative Adversarial Networks (GANs) have played a very important role in this space by enabling the generation of sharp, high-quality images and videos. Works such as https://thispersondoesnotexist.com, based on StyleGAN, have really pushed the boundaries in terms of the generation of high-quality realistic content. A number of other key architectures...

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