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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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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

An Introduction to Generative AI: "Drawing" Data from Models

In this chapter, we will dive into the various applications of generative models. Before that, we will take a step back and examine how exactly generative models are different from other types of machine learning. The difference lies with the basic units of any machine learning algorithm: probability and the various ways we use mathematics to quantify the shape and distribution of data we encounter in the world.

In the rest of this chapter, we will cover:

  • Applications of AI
  • Discriminative and generative models
  • Implementing generative models
  • The rules of probability
  • Why use generative models?
  • Unique challenges of generative models
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