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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
Author Profile Icon Koki Saitoh
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

5. Backpropagation

The previous chapter described neural network training. There, the gradient of a weight parameter in the neural network (i.e., the gradient of the loss function for a weight parameter) was obtained by using numerical differentiation. Numerical differentiation is simple, and its implementation is easy, but it has the disadvantage that calculation takes time. This chapter covers backpropagation, which is a more efficient way to calculate the gradients of weight parameters.

There are two ways to understand backpropagation correctly. One of them uses "equations," while the other uses computational graphs. The former is a common way, and many books about machine learning expand on this by focusing on formulas. This is good because it is strict and simple, but it may hide essential details or end in a meaningless list of equations.

Therefore, this chapter will use computational graphs so that you can understand backpropagation "visually.&quot...

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