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Generative AI with Python and TensorFlow 2

You're reading from   Generative AI with Python and TensorFlow 2 Create images, text, and music with VAEs, GANs, LSTMs, Transformer models

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Product type Paperback
Published in Apr 2021
Publisher Packt
ISBN-13 9781800200883
Length 488 pages
Edition 1st Edition
Languages
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Authors (2):
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Raghav Bali Raghav Bali
Author Profile Icon Raghav Bali
Raghav Bali
Joseph Babcock Joseph Babcock
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Joseph Babcock
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Toc

Table of Contents (16) Chapters Close

Preface 1. An Introduction to Generative AI: "Drawing" Data from Models 2. Setting Up a TensorFlow Lab FREE CHAPTER 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 with GANs

Manipulating videos and photographs to edit artifacts has been in practice for quite a long time. If you have seen movies like Forrest Gump or Fast and Furious 7, chances are you did not even notice that the scenes with John F. Kennedy or Paul Walker in their respective movies were fake and edited into the movies as required.

You may recall one particular scene from the movie Forrest Gump, where Gump meets John F. Kennedy. The scene was created using complex visual effects and archival footage to ensure high-quality results. Hollywood studios, spy agencies from across the world, and media outlets have been making use of editing tools such as Photoshop, After Effects, and complex custom visual effects/CGI (computer generated imagery) pipelines to come up with such compelling results. While the results have been more or less believable in most instances, it takes a huge amount of manual effort and time to edit each and every detail, such...

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