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首页Deep Learning, Vol. 2 From Basics to Practice

Deep Learning, Vol. 2 From Basics to Practice

深度学习

人工智能

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– Volume 2 – 20 Deep Learning 21 Convolutional Neural Nets (CNNs) 22 Recurrent Nerual Nets (RNNs) 23 Keras Part 1 24 Keras Part 2 25 Autoencoders 26 Reinforcement Learning 27 Generative Adversarial Networks (GANs) 28 Creative Applications 29 Datasets 30 Glossary

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Volume 2

Andrew Glassner

DEEP LEARNING:

From Basics

to Practice

Deep Learning:

From Basics to Practice

Volume 2

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,

arising 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

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