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Deep Learning with TensorFlow and Keras – 3rd edition

You're reading from   Deep Learning with TensorFlow and Keras – 3rd edition Build and deploy supervised, unsupervised, deep, and reinforcement learning models

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Product type Paperback
Published in Oct 2022
Publisher Packt
ISBN-13 9781803232911
Length 698 pages
Edition 3rd Edition
Tools
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Authors (3):
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Sujit Pal Sujit Pal
Author Profile Icon Sujit Pal
Sujit Pal
Antonio Gulli Antonio Gulli
Author Profile Icon Antonio Gulli
Antonio Gulli
Dr. Amita Kapoor Dr. Amita Kapoor
Author Profile Icon Dr. Amita Kapoor
Dr. Amita Kapoor
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Toc

Table of Contents (23) Chapters Close

Preface 1. Neural Network Foundations with TF 2. Regression and Classification FREE CHAPTER 3. Convolutional Neural Networks 4. Word Embeddings 5. Recurrent Neural Networks 6. Transformers 7. Unsupervised Learning 8. Autoencoders 9. Generative Models 10. Self-Supervised Learning 11. Reinforcement Learning 12. Probabilistic TensorFlow 13. An Introduction to AutoML 14. The Math Behind Deep Learning 15. Tensor Processing Unit 16. Other Useful Deep Learning Libraries 17. Graph Neural Networks 18. Machine Learning Best Practices 19. TensorFlow 2 Ecosystem 20. Advanced Convolutional Neural Networks 21. Other Books You May Enjoy
22. Index

What is a GAN?

The ability of GANs to learn high-dimensional, complex data distributions has made them very popular with researchers in recent years. Between 2016, when they were first proposed by Ian Goodfellow, to March 2022, we have more than 100,000 research papers related to GANs, just in the space of 6 years!

The applications of GANs include creating images, videos, music, and even natural languages. They have been employed in tasks like image-to-image translation, image super-resolution, drug discovery, and even next-frame prediction in video. They have been especially successful in the task of synthetic data generation – both for training the deep learning models and assessing the adversarial attacks.

The key idea of GAN can be easily understood by considering it analogous to “art forgery,” which is the process of creating works of art that are falsely credited to other usually more famous artists. GANs train two neural nets simultaneously. The...

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