TensorFlow 入门书籍最好.pdf

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Machine Learning with TensorFlow

TensorFlow

brief contents

contents

preface

acknowledgments

about this book

Roadmap

Source code

Note to print book readers

Book forum

about the author

about the cover

Part 1—Your machine-learning rig

1 A machine-learning odyssey

1.1 Machine-learning fundamentals

1.1.1 Parameters

1.1.2 Learning and inference

1.2 Data representation and features

1.3 Distance metrics

1.4 Types of learning

1.4.1 Supervised learning

1.4.2 Unsupervised learning

1.4.3 Reinforcement learning

1.5 TensorFlow

1.6 Overview of future chapters

1.7 Summary

2 TensorFlow essentials

2.1 Ensuring that TensorFlow works

2.2 Representing tensors

2.3 Creating operators

2.4 Executing operators with sessions

2.4.1 Understanding code as a graph

2.4.2 Setting session configurations

2.5 Writing code in Jupyter

2.6 Using variables

2.7 Saving and loading variables

2.8 Visualizing data using TensorBoard

2.8.1 Implementing a moving average

2.8.2 Visualizing the moving average

2.9 Summary

Part 2—Core learning algorithms

3 Linear regression and beyond

3.1 Formal notation

3.1.1 How do you know the regression algorithm is working?

3.2 Linear regression

3.3 Polynomial model

3.4 Regularization

3.5 Application of linear regression

3.6 Summary

4 A gentle introduction to classification

4.1 Formal notation

4.2 Measuring performance

4.2.1 Accuracy

4.2.2 Precision and recall

4.2.3 Receiver operating characteristic curve

4.3 Using linear regression for classification

4.4 Using logistic regression

4.4.1 Solving one-dimensional logistic regression

4.4.2 Solving two-dimensional logistic regression

4.5 Multiclass classifier

4.5.1 One-versus-all

4.5.2 One-versus-one

4.5.3 Softmax regression

4.6 Application of classification

4.7 Summary

5 Automatically clustering data

5.1 Traversing files in TensorFlow

5.2 Extracting features from audio

5.3 K-means clustering

5.4 Audio segmentation

5.5 Clustering using a self-organizing map

5.6 Application of clustering

5.7 Summary

6 Hidden Markov models

6.1 Example of a not-so-interpretable model

6.2 Markov model

6.3 Hidden Markov model

6.4 Forward algorithm

6.5 Viterbi decoding

6.6 Uses of hidden Markov models

6.6.1 Modeling a video

6.6.2 Modeling DNA

6.6.3 Modeling an image

6.7 Application of hidden Markov models

6.8 Summary

Part 3—The neural network paradigm

7 A peek into autoencoders

7.1 Neural networks

7.2 Autoencoders

7.3 Batch training

7.4 Working with images

7.5 Application of autoencoders

7.6 Summary

8 Reinforcement learning

8.1 Formal notions

8.1.1 Policy

8.1.2 Utility

8.2 Applying reinforcement learning

8.3 Implementing reinforcement learning

8.4 Exploring other applications of reinforcement learning

8.5 Summary

9 Convolutional neural networks

9.1 Drawback of neural networks

9.2 Convolutional neural networks

9.3 Preparing the image

9.3.1 Generating filters

9.3.2 Convolving using filters

9.3.3 Max pooling

9.4 Implementing a convolutional neural network in TensorFlow

9.4.1 Measuring performance

9.4.2 Training the classifier

9.5 Tips and tricks to improve performance

9.6 Application of convolutional neural networks

9.7 Summary

10 Recurrent neural networks

10.1 Contextual information

10.2 Introduction to recurrent neural networks

10.3 Implementing a recurrent neural network

10.4 A predictive model for time-series data

10.5 Application of recurrent neural networks

10.6 Summary

11 Sequence-to-sequence models for chatbots

11.1 Building on classification and RNNs

11.2 Seq2seq architecture

11.3 Vector representation of symbols

11.4 Putting it all together

11.5 Gathering dialogue data

11.6 Summary

12 Utility landscape

12.1 Preference model

12.2 Image embedding

12.3 Ranking images

12.4 Summary

12.5 What’s next?

Appendix—Installation

A.1 Installing TensorFlow by using Docker

A.1.1 Installing Docker on Windows

A.1.2 Installing Docker on Linux

A.1.3 Installing Docker on macOS

A.1.4 How to use Docker

A.2 Installing Matplotlib

index

Symbols

Nishant Shukla with Kenneth Fricklas M A N N I N G

Machine Learning with TensorFlow Licensed to Eduardo Guamán

Licensed to Eduardo Guamán

Machine Learning with TensorFlow NISHANT SHUKLA WITH KENNETH FRICKLAS M A N N I N G SHELTER ISLAND Licensed to Eduardo Guamán

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 ©2018 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. Recognizing the importance of preserving what has been written, it is Manning’s 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. Manning Publications Co. 20 Baldwin Road PO Box 761 Shelter Island, NY 11964 Development editor: Toni Arritola Technical development editor: Jerry Gaines Review editor: Aleksandar Dragosavljevic´ Project editor: Tiffany Taylor Copy editor: Sharon Wilkey Proofreader: Katie Tennant Technical proofreader: David Fombella Pombal Typesetter: Dennis Dalinnik Cover designer: Marija Tudor ISBN: 9781617293870 Printed in the United States of America 1 2 3 4 5 6 7 8 9 10 – EBM – 23 22 21 20 19 18 Licensed to Eduardo Guamán

brief contents PART 1 YOUR MACHINE-LEARNING RIG.......................................1 1 ■ A machine-learning odyssey 3 2 ■ TensorFlow essentials 25 PART 2 CORE LEARNING ALGORITHMS .....................................51 3 ■ Linear regression and beyond 53 4 ■ A gentle introduction to classification 71 5 ■ Automatically clustering data 99 6 ■ Hidden Markov models 119 PART 3 THE NEURAL NETWORK PARADIGM .............................133 7 ■ A peek into autoencoders 135 8 ■ Reinforcement learning 153 9 ■ Convolutional neural networks 169 10 ■ Recurrent neural networks 189 11 ■ Sequence-to-sequence models for chatbots 201 12 ■ Utility landscape 223 v Licensed to Eduardo Guamán

Licensed to Eduardo Guamán

contents xiii preface acknowledgments about this book about the author about the cover xv xvii xix xx PART 1 YOUR MACHINE-LEARNING RIG.............................1 1 A machine-learning odyssey 3 1.1 Machine-learning fundamentals 5 Parameters 7 ■ Learning and inference 8 1.2 Data representation and features 9 1.3 Distance metrics 15 1.4 Types of learning 17 Supervised learning 17 ■ Unsupervised learning 19 Reinforcement learning 19 1.5 TensorFlow 21 1.6 Overview of future chapters 22 1.7 Summary 24 vii Licensed to Eduardo Guamán

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