Python深度学习入门指南：Chollet带你入门

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acknowledgments

I’d like to thank the Keras community for making this book possible. Keras has grown

to have hundreds of open source contributors and more than 200,000 users. Your con-

tributions and feedback have turned Keras into what it is today.

I’d also like to thank Google for backing the Keras project. It has been fantastic to

see Keras adopted as TensorFlow’s high-level

API. A smooth integration between Keras

and TensorFlow greatly benefits both TensorFlow users and Keras users and makes

deep learning accessible to most.

I want to thank the people at Manning who made this book possible: publisher

Marjan Bace and everyone on the editorial and production teams, including Christina

Taylor, Janet Vail, Tiffany Taylor, Katie Tennant, Dottie Marsico, and many others who

worked behind the scenes.

Many thanks go to the technical peer reviewers led by Aleksandar Dragosavljevic

´ —

Diego Acuña Rozas, Geoff Barto, David Blumenthal-Barby, Abel Brown, Clark Dor-

man, Clark Gaylord, Thomas Heiman, Wilson Mar, Sumit Pal, Vladimir Pasman, Gus-

tavo Patino, Peter Rabinovitch, Alvin Raj, Claudio Rodriguez, Srdjan Santic, Richard

Tobias, Martin Verzilli, William E. Wheeler, and Daniel Williams—and the forum con-

tributors. Their contributions included catching technical mistakes, errors in termi-

nology, and typos, and making topic suggestions. Each pass through the review

process and each piece of feedback implemented through the forum topics shaped

and molded the manuscript.

On the technical side, special thanks go to Jerry Gaines, who served as the book’s

technical editor; and Alex Ott and Richard Tobias, who served as the book’s technical

proofreaders. They’re the best technical editors I could have hoped for.

Finally, I’d like to express my gratitude to my wife Maria for being extremely

supportive throughout the development of Keras and the writing of this book.

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ABOUT THIS BOOK xvii

Who should read this book

This book is written for people with Python programming experience who want to get

started with machine learning and deep learning. But this book can also be valuable

to many different types of readers:

 If you’re a data scientist familiar with machine learning, this book will provide

you with a solid, practical introduction to deep learning, the fastest-growing

and most significant subfield of machine learning.

 If you’re a deep-learning expert looking to get started with the Keras frame-

work, you’ll find this book to be the best Keras crash course available.

 If you’re a graduate student studying deep learning in a formal setting, you’ll

find this book to be a practical complement to your education, helping you

build intuition around the behavior of deep neural networks and familiarizing

you with key best practices.

Even technically minded people who don’t code regularly will find this book useful as

an introduction to both basic and advanced deep-learning concepts.

In order to use Keras, you’ll need reasonable Python proficiency. Additionally, famil-

iarity with the Numpy library will be helpful, although it isn’t required. You don’t need

previous experience with machine learning or deep learning: this book covers from

scratch all the necessary basics. You don’t need an advanced mathematics background,

either—high school–level mathematics should suffice in order to follow along.

Roadmap

This book is structured in two parts. If you have no prior experience with machine

learning, I strongly recommend that you complete part 1 before approaching part 2.

We’ll start with simple examples, and as the book goes on, we’ll get increasingly close

to state-of-the-art techniques.

Part 1 is a high-level introduction to deep learning, providing context and defini-

tions, and explaining all the notions required to get started with machine learning

and neural networks:

 Chapter 1 presents essential context and background knowledge around AI,

machine learning, and deep learning.

 Chapter 2 introduces fundamental concepts necessary in order to approach

deep learning: tensors, tensor operations, gradient descent, and backpropaga-

tion. This chapter also features the book’s first example of a working neural

network.

 Chapter 3 includes everything you need to get started with neural networks: an

introduction to Keras, our deep-learning framework of choice; a guide for set-

ting up your workstation; and three foundational code examples with detailed

explanations. By the end of this chapter, you’ll be able to train simple neural

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ABOUT THIS BOOKxviii

networks to handle classification and regression tasks, and you’ll have a solid

idea of what’s happening in the background as you train them.

 Chapter 4 explores the canonical machine-learning workflow. You’ll also learn

about common pitfalls and their solutions.

Part 2 takes an in-depth dive into practical applications of deep learning in computer

vision and natural-language processing. Many of the examples introduced in this part

can be used as templates to solve problems you’ll encounter in the real-world practice

of deep learning:

 Chapter 5 examines a range of practical computer-vision examples, with a focus

on image classification.

 Chapter 6 gives you practice with techniques for processing sequence data, such

as text and timeseries.

 Chapter 7 introduces advanced techniques for building state-of-the-art deep-

learning models.

 Chapter 8 explains generative models: deep-learning models capable of creat-

ing images and text, with sometimes surprisingly artistic results.

 Chapter 9 is dedicated to consolidating what you’ve learned throughout the

book, as well as opening perspectives on the limitations of deep learning and

exploring its probable future.

Software/hardware requirements

All of this book’s code examples use the Keras deep-learning framework (https://

keras.io), which is open source and free to download. You’ll need access to a

UNIX

machine; it’s possible to use Windows, too, but I don’t recommend it. Appendix A

walks you through the complete setup.

I also recommend that you have a recent

NVIDIA GPU on your machine, such as a

TITAN X. This isn’t required, but it will make your experience better by allowing you

to run the code examples several times faster. See section 3.3 for more information

about setting up a deep-learning workstation.

If you don’t have access to a local workstation with a recent

NVIDIA GPU, you can

use a cloud environment, instead. In particular, you can use Google Cloud instances

(such as an n1-standard-8 instance with an

NVIDIA Tesla K80 add-on) or Amazon Web

Services (

AWS) GPU instances (such as a p2.xlarge instance). Appendix B presents in

detail one possible cloud workflow that runs an

AWS instance via Jupyter notebooks,

accessible in your browser.

Source code

All code examples in this book are available for download as Jupyter notebooks from

the book’s website, www.manning.com/books/deep-learning-with-python, and on

GitHub at https://github.com/fchollet/deep-learning-with-python-notebooks.

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