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Hands-On Neural Networks

You're reading from   Hands-On Neural Networks Learn how to build and train your first neural network model using Python

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Product type Paperback
Published in May 2019
Publisher Packt
ISBN-13 9781788992596
Length 280 pages
Edition 1st Edition
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Authors (2):
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Leonardo De Marchi Leonardo De Marchi
Author Profile Icon Leonardo De Marchi
Leonardo De Marchi
Laura Mitchell Laura Mitchell
Author Profile Icon Laura Mitchell
Laura Mitchell
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Toc

Table of Contents (16) Chapters Close

Preface 1. Section 1: Getting Started FREE CHAPTER
2. Getting Started with Supervised Learning 3. Neural Network Fundamentals 4. Section 2: Deep Learning Applications
5. Convolutional Neural Networks for Image Processing 6. Exploiting Text Embedding 7. Working with RNNs 8. Reusing Neural Networks with Transfer Learning 9. Section 3: Advanced Applications
10. Working with Generative Algorithms 11. Implementing Autoencoders 12. Deep Belief Networks 13. Reinforcement Learning 14. Whats Next? 15. Other Books You May Enjoy

Summary

In this chapter, we explained the key differences between a generator and a discriminator and how these form the foundations of a GAN. We covered different variations and architectures of GANs and their applications, along with showcasing some of their results.

The GAN revolution has produced some major technological breakthroughs in recent times. Pioneering AI techniques such as GANs are rapidly changing our relationships with technology and with one another; GANs are most certainly something to keep an eye on!

In the next chapter, we will be looking at implementing AEs and their different types.

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