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# 《Neural Networks and Deep Learning》（美）Michael Nielsen 著 英文版.pdf

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《Neural Networks and Deep Learning》（美）Michael Nielsen 著 英文版

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2016/10/10 Neural networks and deep learning

http://neuralnetworksanddeeplearning.com/index.html 1/2

NeuralNetworksandDeepLearning is a free online book. The

book will teach you about:

Neural networks, a beautiful biologically-inspired

programming paradigm which enables a computer to learn

from observational data

Deep learning, a powerful set of techniques for learning in

neural networks

Neural networks and deep learning currently provide the best

solutions to many problems in image recognition, speech

recognition, and natural language processing. This book will teach

you many of the core concepts behind neural networks and deep

learning.

For more details about the approach taken in the book, see here. Or

you can jump directly to Chapter 1 and get started.

Neural Networks and Deep Learning

Neural Networks and Deep earning

hat this book is about

On the eercises and problems

sing neural nets to recognie

handwritten digits

ow the backpropagation

algorithm works

mproving the way neural

networks learn

visual proof that neural nets can

compute any function

hy are deep neural networks

hard to train

Deep learning

ppendi: s there a siple

algorithm for intelligence

cknowledgements

Frequently sked uestions

f you benefit from the book, please

make a small donation. suggest ,

but you can choose the amount.

Sponsors

Thanks to all the supporters who

made the book possible, with

especial thanks to avel Dudrenov.

Thanks also to all the contributors to

the ugfinder all of Fame.

Resources

ook F

Code repository

ichael Nielsens project

announcement mailing list

Deep earning, draft book in

preparation, by oshua engio, an

oodfellow, and aron Courville

欧拉的博客:www.liuhao.me

2016/10/10 Neural networks and deep learning

http://neuralnetworksanddeeplearning.com/index.html 2/2

y ichael Nielsen an 1

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eernonress

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ourereeocopsrenuonsoounooseoureneresencoercusepese

conce

supern

欧拉的博客:www.liuhao.me

2016/10/10 Neural networks and deep learning

http://neuralnetworksanddeeplearning.com/aout.html 1/

Neural networks are one of the most beautiful programming

paradigms ever invented. n the conventional approach to

programming, we tell the computer what to do, breaking big

problems up into many small, precisely defined tasks that the

computer can easily perform. y contrast, in a neural network we

dont tell the computer how to solve our problem. nstead, it learns

from observational data, figuring out its own solution to the

p

roblem at hand.

utomatically learning from data sounds promising. owever, until

we didnt know how to train neural networks to surpass more

traditional approaches, ecept for a few specialied problems. hat

changed in was the discovery of techniques for learning in so-

called deep neural networks. These techniques are now known as

deep learning. Theyve been developed further,

and today deep

neural networks and deep learning achieve outstanding

performance on many important problems in computer vision,

speech recognition, and natural language processing. Theyre being

deployed on a large scale by companies such as oogle, icrosoft,

and Facebook.

The purpose of this book is to help you master the core concepts of

neural networks, including modern techniques for deep learning.

fter working through the book you will have written code that uses

neural networks and deep learning to solve comple pattern

recognition problems. nd you will have a foundation to use neural

networks and deep learning to attack problems of your own

devising.

prinipleoriented approa

One conviction underlying the book is that its better to obtain a

solid understanding of the core principles of neural networks and

d

eep learning, rather than a hay understanding of a long laundry

list of ideas. f youve understood the core ideas well, you can

rapidly understand other new material. n programming language

terms, think of it as mastering the core synta, libraries and data

structures of a new language. ou may still only know a tiny

at tis ook is aout

Neural Networks and Deep earning

hat this book is about

On the eercises and problems

sing neural nets to recognie

handwritten digits

ow the backpropagation

algorithm works

mproving the way neural

networks learn

visual proof that neural nets can

compute any function

hy are deep neural networks

hard to train

Deep learning

ppendi: s there a siple

algorithm for intelligence

cknowledgements

Frequently sked uestions

f you benefit from the book, please

make a small donation. suggest ,

but you can choose the amount.

Sponsors

Thanks to all the supporters who

made the book possible, with

especial thanks to avel Dudrenov.

Thanks also to all the contributors to

the ugfinder all of Fame.

Resources

ook F

Code repository

ichael Nielsens project

announcement mailing list

Deep earning, draft book in

preparation, by oshua engio, an

oodfellow, and aron Courville

欧拉的博客:www.liuhao.me

2016/10/10 Neural networks and deep learning

http://neuralnetworksanddeeplearning.com/aout.html 2/

fraction of the total language - many languages have enormous

standard libraries - but new libraries and data structures can be

understood quickly and easily.

This means the book is emphatically not a tutorial in how to use

some particular neural network library. f you mostly want to learn

your way around a library, dont read this book Find the library you

wish to learn, and work through the tutorial

s and documentation.

ut be warned. hile this has an immediate problem-solving

payoff, if you want to understand whats really going on in neural

networks, if you want insights that will still be relevant years from

now, then its not enough just to learn some hot library. ou need to

understand the durable, lasting insights underlying how neural

networks work. Technologies come and technologies go, bu

t insight

is forever.

andson approa

ell learn the core principles behind neural networks and deep

learning by attacking a concrete problem: the problem of teaching a

computer to recognie handwritten digits. This problem is

etremely difficult to solve using the conventional approach to

programming. nd yet, as well see, it can be solved pretty well

using a simple neural network, with just a few

tens of lines of code,

and no special libraries. hats more, well improve the program

through many iterations, gradually incorporating more and more of

the core ideas about neural networks and deep learning.

This hands-on approach means that youll need some programming

eperience to read the book. ut you dont need to be a professional

programmer. ve written the code in ython version ., which

,

even if you dont program in ython, should be easy to understand

with just a little effort. Through the course of the book we will

develop a little neural network library, which you can use to

eperiment and to build understanding. ll the code is available for

download here. Once youve finished the book, or as you read it, you

can easily pick up one of the more feature-complete neural network

librar

ies intended for use in production.

On a related note, the mathematical requirements to read the book

are modest. There is some mathematics in most chapters, but its

y ichael Nielsen an 1

欧拉的博客:www.liuhao.me

2016/10/10 Neural networks and deep learning

http://neuralnetworksanddeeplearning.com/aout.html /

usually just elementary algebra and plots of functions, which

epect most readers will be okay with. occasionally use more

advanced mathematics, but have structured the material so you can

follow even if some mathematical details elude you. The one

chapter which uses heavier mathematics etensively is Chapter ,

which requires a little multivariable calculus and linear algebra. f

those arent famil

iar, begin Chapter with a discussion of how to

navigate the mathematics. f youre finding it really heavy going,

you can simply skip to the summary of the chapters main results.

n any case, theres no need to worry about this at the outset.

ts rare for a book to aim to be both principle-oriented and hands-

on. ut believe youll learn best if we build out the fundamental

ideas of neural netwo

rks. ell develop living code, not just abstract

theory, code which you can eplore and etend. This way youll

understand the fundamentals, both in theory and practice, and be

well set to add further to your knowledge.

ncecorpesecesoosceeseneureorsneepernn

eernonress

sorscenseunerreeoonsruononoercnporecensesens

ourereeocopsrenuonsoounooseoureneresencoercusepese

conce

supern

欧拉的博客:www.liuhao.me

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