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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

Capsule networks

Capsule networks were introduced by Geoffrey Hinton as a way to overcome some of the limitations of standard CNNs. To understand the idea behind capsule networks, we need to understand these limitations first.

Limitations of convolutional networks

Let's start with a quote from professor Hinton himself:

"The pooling operation used in convolutional neural networks is a big mistake and the fact that it works so well is a disaster."

What he means is that the CNNs are translation-invariant. To understand this, let's imagine a picture with a face, located in the right half of the picture. Translation invariance means that a CNN is very good at telling us that the picture contains a face, but...

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