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Python Deep Learning

You're reading from   Python Deep Learning Exploring deep learning techniques and neural network architectures with PyTorch, Keras, and TensorFlow

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Product type Paperback
Published in Jan 2019
Publisher Packt
ISBN-13 9781789348460
Length 386 pages
Edition 2nd Edition
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Authors (5):
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Gianmario Spacagna Gianmario Spacagna
Author Profile Icon Gianmario Spacagna
Gianmario Spacagna
Daniel Slater Daniel Slater
Author Profile Icon Daniel Slater
Daniel Slater
Valentino Zocca Valentino Zocca
Author Profile Icon Valentino Zocca
Valentino Zocca
Peter Roelants Peter Roelants
Author Profile Icon Peter Roelants
Peter Roelants
Ivan Vasilev Ivan Vasilev
Author Profile Icon Ivan Vasilev
Ivan Vasilev
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Toc

Table of Contents (12) Chapters Close

Preface 1. Machine Learning - an Introduction 2. Neural Networks FREE CHAPTER 3. Deep Learning Fundamentals 4. Computer Vision with Convolutional Networks 5. Advanced Computer Vision 6. Generating Images with GANs and VAEs 7. Recurrent Neural Networks and Language Models 8. Reinforcement Learning Theory 9. Deep Reinforcement Learning for Games 10. Deep Learning in Autonomous Vehicles 11. Other Books You May Enjoy

The reasons for deep learning's popularity

If you've followed machine learning for some time, you may have noticed that many DL algorithms are not new. We dropped some hints about this in the A brief history of contemporary deep learning section, but let's see some more examples now. Multilayer perceptrons have been around for nearly 50 years. Backpropagation has been discovered a couple of times, but finally gained recognition in 1986. Yann LeCun, a famous computer scientist, perfected his work on convolutional networks in the 1990s. In 1997, Sepp Hochreiter and Jürgen Schmidhuber invented long short-term memory, a type of recurrent neural network still in use today. In this section, we'll try to understand why we have AI summer now, and why we only had AI winters(https://en.wikipedia.org/wiki/AI_winter) before.

The first reason is, today, we have a...

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