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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

2. Perceptrons

This chapter describes an algorithm called a perceptron. Invented by the US researcher Frank Rosenblatt in 1957, it is from this traditional algorithm that neural networks (i.e., deep learning) originated and is thus a necessary first step to the more advanced study of both. This chapter will describe a perceptron and use one to solve easy problems. Throughout this process, you will familiarize yourself with the mechanics of perceptrons.

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