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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 FREE CHAPTER 2. Neural Networks 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

Computer Vision with Convolutional Networks

In Chapter 2, Neural Networks, and Chapter 3, Deep Learning Fundamentals, we set high expectations of deep learning and computer vision. First, we mentioned the ImageNet competition, and then we talked about some of its exciting real-world applications, such as semi-autonomous cars. In this chapter, and the next two chapters, it's time to deliver on those expectations.

Vision is arguably the most important human sense. We rely on it for almost any action we take. But image recognition has (and in some ways still is), for the longest time, been one of the most difficult problems in computer science. Historically, it's been very difficult to explain to a machine what features make up a specified object, and how to detect them. But, as we've seen, in deep learning the neural network can learn those features by itself.

In...

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