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Andrew W. Trask - Grokking Deep Learning

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Andrew W. Trask Grokking Deep Learning
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Grokking Deep Learning: summary, description and annotation

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Artificial Intelligence is one of the most exciting technologies of the century, and Deep Learning is in many ways the brain behind some of the worlds smartest Artificial Intelligence systems out there. Loosely based on neuron behavior inside of human brains, these systems are rapidly catching up with the intelligence of their human creators, defeating the world champion Go player, achieving superhuman performance on video games, driving cars, translating languages, and sometimes even helping law enforcement fight crime. Deep Learning is a revolution that is changing every industry across the globe.

Grokking Deep Learning is the perfect place to begin your deep learning journey. Rather than just learn the black box API of some library or framework, you will actually understand how to build these algorithms completely from scratch. You will understand how Deep Learning is able to learn at levels greater than humans. You will be able to understand the brain behind state-of-the-art Artificial Intelligence. Furthermore, unlike other courses that assume advanced knowledge of Calculus and leverage complex mathematical notation, if youre a Python hacker who passed high-school algebra, youre ready to go. And at the end, youll even build an A.I. that will learn to defeat you in a classic Atari game.

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Grokking Deep Learning
Andrew W. Trask

Grokking Deep Learning - image 1

Copyright

For online information and ordering of this and other Manning books, please visit www.manning.com. The publisher offers discounts on this book when ordered in quantity. For more information, please contact

Special Sales Department Manning Publications Co. 20 Baldwin Road, PO Box 761 Shelter Island, NY 11964 Email: orders@manning.com

2019 by Manning Publications Co. All rights reserved.

No part of this publication may be reproduced, stored in a retrieval system, or transmitted, in any form or by means electronic, mechanical, photocopying, or otherwise, without prior written permission of the publisher.

Many of the designations used by manufacturers and sellers to distinguish their products are claimed as trademarks. Where those designations appear in the book, and Manning Publications was aware of a trademark claim, the designations have been printed in initial caps or all caps.

Picture 2 Recognizing the importance of preserving what has been written, it is Mannings policy to have the books we publish printed on acid-free paper, and we exert our best efforts to that end. Recognizing also our responsibility to conserve the resources of our planet, Manning books are printed on paper that is at least 15 percent recycled and processed without the use of elemental chlorine.

Picture 3Manning Publications Co.20 Baldwin RoadShelter Island, NY 11964
Development editor: Christina TaylorReview editor: Aleksandar DragosavljevicProduction editor: Lori WeidertCopyeditor: Tiffany TaylorProofreader: Sharon WilkeyTechnical proofreader: David Fombella PomballTypesetter: Dennis DalinnikCover designer: Leslie Haimes

ISBN: 9781617293702

Printed in the United States of America

1 2 3 4 5 6 7 8 9 10 SP 23 22 21 20 19 18

Dedication

To Mom. You sacrificed so much time in your life to bless Tara and me with education. I hope you see your work behind this book.

And to Dad. Thank you for loving us so much and for taking the time to teach me programming and technology at such a young age. I wouldnt be doing this without you.

It is a great honor to be your son.

Brief Table of Contents
Table of Contents
Preface

Grokking Deep Learning is the product of a monumental three years of effort. To get to the book you hold in your hand, I wrote at least twice the number of pages you see here. Half-a-dozen chapters were rewritten from scratch three or four times before they were ready to publish, and along the way important chapters were added that werent part of the original plan.

More significantly, I arrived at two decisions early on that make Grokking Deep Learning uniquely valuable: this book requires no math background beyond basic arithmetic, and it doesnt rely on a high-level library that might hide what is going on. In other words, anyone can read this book and understand how deep learning really works. To accomplish this, I had to invent new ways to describe and teach the core ideas and techniques without falling back on advanced mathematics or sophisticated code that someone else wrote.

My goal in writing Grokking Deep Learning was to create the lowest possible barrier to entry to the practice of deep learning. You dont just read the theory; youll discover it yourself. To help you get there, To help you get there, I wrote a lot of code and did my best to explain it in the right order so that the code snippets required for the working demos all made sense.

This knowledge, combined with all the theory, code, and examples youll explore in this book, will make you much faster at iterating through experiments. Youll have quick successes and better job opportunities, and youll even learn about more-advanced deep learning concepts more rapidly.

In the last three years, I not only authored this book, but also entered a PhD program at Oxford, joined the team at Google, and helped spearhead OpenMined, a decentralized artificial intelligence platform. This book is the culmination of years of thinking, learning, and teaching.

There are many other resources for learning deep learning. Im glad that you came to this one.

Acknowledgments

Im exceedingly grateful for everyone who has contributed to the production of Grokking Deep Learning. First and foremost, Id like to thank the amazing team at Manning: Bert Bates, who taught me how to write; Christina Taylor, who patiently kept me going for three years; Michael Stephens, whose creativity has allowed the book to have great success even before publication; and Marjan Bace, whose encouragement in the midst of delays made all the difference.

Grokking Deep Learning wouldnt be what it is without the immense contributions of early readers through email, Twitter, and GitHub. I feel greatly indebted to Jascha Swisher, Varun Sudhakar, Francois Chollet, Frederico Vitorino, Cody Hammond, Mauricio Maroto Arrieta, Aleksandar Dragosavljevic, Alan Carter, Frank Hinek, Nicolas Benjamin Hocker, Hank Meisse, Wouter Hibma, Joerg Rosenkranz, Alex Vieira, and Charlie Harrington for all your help refining the text and the online code repository.

Id like to thank the reviewers who took time to read the manuscript at various stages in development: Alexander A. Myltsev, Amit Lamba, Anand Saha, Andrew Hamor, Cristian Barrientos, Montoya, Eremey Valetov, Gerald Mack, Ian Stirk, Kalyan Reddy, Kamal Raj, Kelvin D. Meeks, Marco Paulo dos Santos Nogueira, Martin Beer, Massimo Ilario, Nancy W. Grady, Peter Hampton, Sebastian Maldonado, Shashank Gupta, Tymoteusz Woodko, Kumar Unnikrishnan, Vipul Gupta, Will Fuger, and William Wheeler.

Im also grateful to Mat and Niko at Udacity, who included the book in Udacitys Deep Learning Nanodegree, which greatly aided in early awareness of the book among young deep learning practitioners.

I must thank Dr. William Hooper, who let me wander into his office and bug him about computer science, who made an exception to let me into his (already full) Programming 1 class, and who inspired me to pursue a career in deep learning. I am exceedingly thankful for all the patience you had with me starting out. You have blessed me immensely.

Finally, Id like to thank my wife for being so patient with me during all the nights and weekends spent working on the book, for copyediting the entire text several times herself, and for creating and debugging the online GitHub code repository.

About this book

Grokking Deep Learning was written to help give you a foundation in deep learning so that you can master a major deep learning framework. It begins by focusing on the basics of neural networks and then switches its focus to provide an in-depth look at advanced layers and architectures.

Who should read this book

Ive intentionally written this book with what I believe is the lowest barrier to entry possible. No knowledge of linear algebra, calculus, convex optimization, or even machine learning is assumed. Everything from those subjects thats necessary to understand deep learning will be explained as we go. If youve passed high school mathematics and hacked around in Python, youre ready for this book.

Roadmap

This book has 16 chapters:

  • focuses on why should you learn deep learning, and what youll need to get started.
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