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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

4. Neural Network Training

This chapter describes neural network training. When we talk about "training" in this context, we mean obtaining the optimal weight parameters automatically from training data. In this chapter, we will introduce a criterion called a loss function; this enables a neural network to learn. The purpose of training is to discover the weight parameters that lead to the smallest value of the loss function. In this chapter, we will be introduced to the method of using the gradient of a function, called a gradient method, to discover the smallest loss function value.

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