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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

Product type Paperback

Published in Mar 2021

Publisher Packt

ISBN-13 9781800206137

Length 316 pages

Edition 1st Edition

Languages

Python

Concepts

Deep Learning

Authors (2):

Shigeo Yushita

Koki Saitoh

View More author details

Table of Contents (11) Chapters

Preface

About the Book

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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Authors (2)

Shigeo Yushita

Shigeo Yushita

Contracted

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Saitoh

Saitoh

Koki Saitoh was born in Tsushima, Nagasaki in 1984. He graduated from the engineering department of the Tokyo Institute of Technology and completed a master's course at the Graduate School of Interdisciplinary Information Studies at the University of Tokyo. Currently, he conducts research and development in computer vision and machine learning. He has authored Python 3 in Practice, The Elements of Computing Systems, and Building Machine Learning Systems with Python, translations of which are published by O'Reilly, Japan.

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