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Deep Learning from the Basics

You're reading from   Deep Learning from the Basics Python and Deep Learning: Theory and Implementation

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Product type Paperback
Published in Mar 2021
Publisher Packt
ISBN-13 9781800206137
Length 316 pages
Edition 1st Edition
Languages
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Authors (2):
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Shigeo Yushita Shigeo Yushita
Author Profile Icon Shigeo Yushita
Shigeo Yushita
Koki Saitoh Koki Saitoh
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Koki Saitoh
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Toc

Table of Contents (11) Chapters Close

Preface Introduction 1. Introduction to Python FREE CHAPTER 2. Perceptrons 3. Neural Networks 4. Neural Network Training 5. Backpropagation 6. Training Techniques 7. Convolutional Neural Networks 8. Deep Learning Appendix A

The Pooling Layer

A pooling operation makes the space of the height and width smaller. As shown in Figure 7.14, it converts a 2 x 2 area into one element to reduce the space's size:

Figure 7.14: Procedure of max pooling

This example shows this procedure when 2 x 2 max-pooling is conducted with a stride of 2. "Max pooling" takes the maximum value of a region, while "2 x 2" indicates the size of the target region. As we can see, it takes the maximum element in a 2 x 2 region. The stride is 2 in this example, so the 2 x 2 window moves by two elements at one time. Generally, the same value is used for the pooling window size and the stride. For example, the stride is 3 for a 3 x 3 window, and the stride is 4 for a 4 x 4 window.

Note

In addition to max pooling, average pooling can also be used. Max pooling takes the maximum value in the target region, while average pooling averages the values in the target region. In image recognition...

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