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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

Pooling layers

In the previous section, we explained how to increase the receptive field of the neurons by using a stride larger than 1. But we can also do this with the help of pooling layers. A pooling layer splits the input slice into a grid, where each grid cell represents a receptive field of a number of neurons (just as a convolutional layer does). Then, a pooling operation is applied over each cell of the grid. Different types of pooling layers exist. Pooling layers don't change the volume depth, because the pooling operation is performed independently on each slice.

Max pooling: is the most popular way of pooling. The max pooling operation takes the neuron with the highest activation value in each local receptive field (grid cell), and propagates only that value forward. In the following figure, we can see an example of max pooling with a receptive field of 2x2:

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