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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

Acknowledgments

First, I would like to thank the researchers and engineers who have conducted research of technologies about deep learning: machine learning and computer science. It is thanks to them that I can write this book. I also thank those who have published useful information in books and on the Web. Above all, I learned a lot from the spirit of generously providing useful technologies and information in the open class CS231n (Convolutional Neural Networks for Visual Recognition (http://cs231n.github.io/) at Stanford University.

The following people have contributed to my writing. Tetsuro Kato, Shinya Kita, Yuka Tobinaga, Kota Nakano, Masatatu Nakamura, Akihiro Hayashi, and Ryo Yamamoto at teamLab, Inc., Kenshi Muto and Moe Masuko at Top Studio Co., Kenji Nomura at Flickfit, and Hidetaka Tanno, JSPS oversea research fellow in The University of Texas at Austin. These people read the manuscript of this book and provided much advice. I would like to thank them here. I say clearly...

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