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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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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

Some interesting GAN architectures

Since their inception, a lot of interest has been generated in GANs, and as a result, we are seeing a lot of modifications and experimentation with GAN training, architecture, and applications. In this section, we will explore some interesting GANs proposed in recent years.

SRGAN

Remember seeing a crime thriller where our hero asks the computer guy to magnify the faded image of the crime scene? With the zoom, we can see the criminal’s face in detail, including the weapon used and anything engraved upon it! Well, Super Resolution GANs (SRGANs) can perform similar magic. Magic in the sense that because GANs show that it is possible to get high-resolution images, the final results depend on the camera resolution used. Here, a GAN is trained in such a way that it can generate a photorealistic high-resolution image when given a low-resolution image. The SRGAN architecture consists of three neural networks: a very deep generator network...

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