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Python Deep Learning

You're reading from   Python Deep Learning Next generation techniques to revolutionize computer vision, AI, speech and data analysis

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Product type Paperback
Published in Apr 2017
Publisher Packt
ISBN-13 9781786464453
Length 406 pages
Edition 1st Edition
Languages
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Authors (4):
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Peter Roelants Peter Roelants
Author Profile Icon Peter Roelants
Peter Roelants
Daniel Slater Daniel Slater
Author Profile Icon Daniel Slater
Daniel Slater
Valentino Zocca Valentino Zocca
Author Profile Icon Valentino Zocca
Valentino Zocca
Gianmario Spacagna Gianmario Spacagna
Author Profile Icon Gianmario Spacagna
Gianmario Spacagna
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Toc

Table of Contents (12) Chapters Close

Preface 1. Machine Learning – An Introduction FREE CHAPTER 2. Neural Networks 3. Deep Learning Fundamentals 4. Unsupervised Feature Learning 5. Image Recognition 6. Recurrent Neural Networks and Language Models 7. Deep Learning for Board Games 8. Deep Learning for Computer Games 9. Anomaly Detection 10. Building a Production-Ready Intrusion Detection System Index

What is deep learning?

In 2012, Alex Krizhevsky, Ilya Sutskever, and Geoff Hinton published an article titled ImageNet Classification with Deep Convolutional Neural Networks in Proceedings of Neural Information Processing Systems (NIPS) (2012) and, at the end of their paper, they wrote:

"It is notable that our network's performance degrades if a single convolutional layer is removed. For example, removing any of the middle layers results in a loss of about 2% for the top-1 performance of the network. So the depth really is important for achieving our results."

In this milestone paper, they clearly mention the importance of the number of hidden layers present in deep networks. Krizheysky, Sutskever, and Hilton talk about convolutional layers, and we will not discuss them until Chapter 5, Image Recognition, but the basic question remains: What do those hidden layers do?

A typical English saying is a picture is worth a thousand words. Let's use this approach to understand...

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