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Volume 1

DEEP LEARNING:

From Basics

to Practice

Andrew Glassner

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Deep Learning:

From Basics to Practice

Volume 1

www.glassner.com / @AndrewGlassner

stored in a retrieval system, or transmitted in any form or by any means, without

the prior written permission of the author, except in the case of brief quotations

embedded in critical articles or reviews.

The above reservation of rights does not apply to the program les associated with

this book (available on GitHub), or to the images and gures (also available on

GitHub), which are released under the MIT license. Any images or gures that are

not original to the author retain their original copyrights and protections, as noted

in the book and on the web pages where the images are provided.

All software in this book, or in its associated repositories, is provided “as is,” with-

out warranty of any kind, express or implied, including but not limited to the

warranties of merchantability, tness for a particular pupose, and noninfringe-

ment. In no event shall the authors or copyright holders be liable for any claim,

damages or other liability, whether in an action of contract, tort, or otherwise, aris-

ing from, out of or in connection with the software or the use or other dealings in

the software.

First published February 20, 2018

Version 1.0.1 March 3, 2018

Version 1.1 March 22, 2018

Published by The Imaginary Institute, Seattle, WA.

http://www.imaginary-institute.com

Contact: andrew@imaginary-institute.com

For Niko,

who’s always there

with a smile

and a wag.

Contents of Both Volumes

Volume 1

Preface ....................................................................i

Chapter 1: An Introduction ................................... 1

1.1 Why This Chapter Is Here ...............................3

1.1.1 Extracting Meaning from Data ............................ 4

1.1.2 Expert Systems .....................................................6

1.2 Learning from Labeled Data ..........................9

1.2.1 A Learning Strategy .............................................. 10

1.2.2 A Computerized Learning Strategy ................... 12

1.2.3 Generalization ...................................................... 16

1.2.4 A Closer Look at Learning ................................... 18

1.3 Supervised Learning ........................................21

1.3.1 Classication ......................................................... 21

1.3.2 Regression ............................................................. 22

1.4 Unsupervised Learning ...................................25

1.4.1 Clustering .............................................................. 25

1.4.2 Noise Reduction ................................................... 26

1.4.3 Dimensionality Reduction .................................. 28

1.5 Generators ........................................................32

1.6 Reinforcement Learning .................................34

1.7 Deep Learning ..................................................37

1.8 What’s Coming Next ....................................... 43

References ..............................................................44

Image credits ................................................................. 45

Chapter 2: Randomness and Basic Statistics .....46

2.1 Why This Chapter Is Here ...............................48

2.2 Random Variables ........................................... 49

2.2.1 Random Numbers in Practice............................. 57

2.3 Some Common Distributions ........................59

2.3.1 The Uniform Distribution ................................... 60

2.3.2 The Normal Distribution .................................... 61

2.3.3 The Bernoulli Distribution ................................. 67

2.3.4 The Multinoulli Distribution .............................. 69

2.3.5 Expected Value .................................................... 70

2.4 Dependence ....................................................70

2.4.1 i.i.d. Variables ........................................................ 71

2.5 Sampling and Replacement ...........................71

2.5.1 Selection With Replacement .............................. 73

2.5.2 Selection Without Replacement ....................... 74

2.5.3 Making Selections ............................................... 75

2.6 Bootstrapping .................................................76

2.7 High-Dimensional Spaces ..............................82

2.8 Covariance and Correlation ...........................85

2.8.1 Covariance ............................................................ 86

2.8.2 Correlation ........................................................... 88

2.9 Anscombe’s Quartet .......................................93

References ..............................................................95

剩余908页未读，继续阅读

Deep Learning, Vol. 2: From Basics to Practice

Deep Learning, Vol. 2: From Basics to Practice By 作者: Andrew Glassner Pub Date: 2018 ISBN: n/a Pages: (914 of 1750) Format: PDF Publication Date: February 19, 2018 Language: English ASIN: B079Y1M81K People are using the tools of deep learning to change how we think about science, art, engineering, business, medicine, and even music. This book is for people who want to understand this field well enough to create deep learning systems, train them, and then use them with confidence to make their own contributions. The book takes a friendly, informal approach. Our goal is to make the ideas of this field simple and accessible to everyone, as shown in the Contents below. Since most practitioners today use one of several free, open-source deep-learning libraries to build their systems, the hard part isn’t in the programming. Rather, it’s knowing what tools to use, and when, and how. Building a working deep learning system requires making a series of technically informed choices, and with today’s tools, those choices require understanding what’s going on under the hood. This book is designed to give you that understanding. You’ll be able to choose the right kind of architecture, how to build a system that can learn, how to train it, and then how to use it to accomplish your goals. You’ll be able to read and understand the documentation for whatever library you’d like to use. And you’ll be able to follow exciting, on-going breakthroughs as they appear, because you’ll have the knowledge and vocabulary that let you read new material, and discuss it with other people doing deep learning. The book is extensively illustrated with over 1000 original figures. They are also all available for free download, for your own use. You don’t need any previous experience with machine learning or deep learning for this book. You don’t need to be a mathematician, because there’s nothing in the book harder than the occasional multiplication. You don’t need to choose a particular programming language, or library, or piece of hardware, because our approach is largely independent of those things. Our focus is on the principles and techniques that are applicable to any language, library, and hardware. Even so, practical programming is important. To stay focused, we gather our programming discussions into 3 chapters that show how to use two important and free Python libraries. Both chapters come with extensive Jupyter notebooks that contain all the code. Other chapters also offer notebooks for for every Python-generated figure. Our goal is to give you all the basics you need to understand deep learning, and then show how to use those ideas to construct your own systems. Everything is covered from the ground up, culminating in working systems illustrated with running code. The book is organized into two volumes. Volume 1 covers the basic ideas that support the field, and which form the core understanding for using these methods well. Volume 2 puts these principles into practice. Deep learning is fast becoming part of the intellectual toolkit used by scientists, artists, executives, doctors, musicians, and anyone else who wants to discover the information hiding in their data, paintings, business reports, test results, musical scores, and more. This friendly, informal book puts those tools into your pocket.

Deep Learning, Vol. 1: From Basics to Practice

Deep Learning, Vol. 1: From Basics to Practice By 作者: Andrew Glassner Pub Date: 2018 ISBN: n/a Pages: (909 of 1750) Language: English Format: PDF People are using the tools of deep learning to change how we think about science, art, engineering, business, medicine, and even music. This book is for people who want to understand this field well enough to create deep learning systems, train them, and then use them with confidence to make their own contributions. The book takes a friendly, informal approach. Our goal is to make the ideas of this field simple and accessible to everyone, as shown in the Contents below. Since most practitioners today use one of several free, open-source deep-learning libraries to build their systems, the hard part isn’t in the programming. Rather, it’s knowing what tools to use, and when, and how. Building a working deep learning system requires making a series of technically informed choices, and with today’s tools, those choices require understanding what’s going on under the hood. This book is designed to give you that understanding. You’ll be able to choose the right kind of architecture, how to build a system that can learn, how to train it, and then how to use it to accomplish your goals. You’ll be able to read and understand the documentation for whatever library you’d like to use. And you’ll be able to follow exciting, on-going breakthroughs as they appear, because you’ll have the knowledge and vocabulary that let you read new material, and discuss it with other people doing deep learning. The book is extensively illustrated with over 1000 original figures. They are also all available for free download, for your own use. You don’t need any previous experience with machine learning or deep learning for this book. You don’t need to be a mathematician, because there’s nothing in the book harder than the occasional multiplication. You don’t need to choose a particular programming language, or library, or piece of hardware, because our approach is largely independent of those things. Our focus is on the principles and techniques that are applicable to any language, library, and hardware. Even so, practical programming is important. To stay focused, we gather our programming discussions into 3 chapters that show how to use two important and free Python libraries. Both chapters come with extensive Jupyter notebooks that contain all the code. Other chapters also offer notebooks for for every Python-generated figure. Our goal is to give you all the basics you need to understand deep learning, and then show how to use those ideas to construct your own systems. Everything is covered from the ground up, culminating in working systems illustrated with running code. The book is organized into two volumes. Volume 1 covers the basic ideas that support the field, and which form the core understanding for using these methods well. Volume 2 puts these principles into practice. Deep learning is fast becoming part of the intellectual toolkit used by scientists, artists, executives, doctors, musicians, and anyone else who wants to discover the information hiding in their data, paintings, business reports, test results, musical scores, and more.

Sanet.st_Deep_Learning,_Vol._2_From_Basics_to_Practice_-_Andrew_Glassner_带书签.pdf

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确保sc_basics_resident表中的house_id、unit_id、building_id、area_id和community_id列以及sc_basics_houses、sc_basics_unit、sc_basics_building、sc_basics_area和sc_basics_community表中对应的列上都有索引。...

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Deep Learning Vol 1 From Basics to Practice_带书签.pdf

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Deep Learning Vol 1 From Basics to Practice_带书签.pdf

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