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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

From NAND to a Computer

Multilayer perceptrons can create more complicated circuits than those we have examined so far. For example, an adder circuit that add numbers can be created with perceptrons. An encoder that converts a binary number into a decimal number and a circuit that outputs 1 when certain conditions are met (circuit for parity checks) can be represented with perceptrons. As a matter of fact, we can even use perceptrons to represent a computer.

A computer is a machine that processes information. When it receives input, a computer processes it in a certain way and outputs the result. Processing in a certain way means that both a computer and a perceptron have inputs and outputs and calculate them based on fixed rules.

Although it seems that a computer conducts very complicated processes inside it, in fact (surprisingly), a combination of NAND gates can reproduce what a computer does. The surprising fact that NAND gates are all we need to create a computer means that...

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