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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

Summary

In this chapter, you saw how deep neural networks can be used to create representations of complex data such as images that capture more of their variance than traditional dimension reduction techniques, such as PCA. This is demonstrated using the MNIST digits, where a neural network can spatially separate the different digits in a two-dimensional grid more cleanly than the principal components of those images. The chapter showed how deep neural networks can be used to approximate complex posterior distributions, such as images, using variational methods to sample from an approximation of an intractable distribution, leading to a VAE algorithm based on minimizing the variational lower bound between the true and approximate posterior.

You also learned how the latent vector from this algorithm can be reparameterized to have lower variance, leading to better convergence in stochastic minibatch gradient descent. You saw how the latent vectors generated by encoders in these...

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