Neural networks and deep learning
Neural networks are one of the most beautiful programming paradigms
ever invented.
In 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.
By
contrast, in a neural network we don't tell the computer how to solve
our problem.
Instead, it learns from observational data, figuring out
its own solution to the problem at hand.Automatically learning from data sounds promising.
However, until
2006 we didn't know how to train neural networks to surpass more
traditional approaches, except for a few specialized problems.
What
changed in 2006 was the discovery of techniques for learning in
so-called deep neural networks.
These techniques are now known as
deep learning.
They've 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.
They're being deployed on a large scale by
companies such as Google, Microsoft, and Facebook.The purpose of this book is to help you master the core concepts of
neural networks, including modern techniques for deep learning.
After
working through the book you will have written code that uses neural
networks and deep learning to solve complex pattern recognition
problems.
And you will have a foundation to use neural networks and
deep learning to attack problems of your own devising.One conviction underlying the book is that it's better to obtain a
solid understanding of the core principles of neural networks and deep
learning, rather than a hazy understanding of a long laundry list of
ideas.
If you've understood the core ideas well, you can rapidly
understand other new material.
In programming language terms, think
of it as mastering the core syntax, libraries and data structures of a
new language.
You may still only "know" a tiny 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.
If you mostly want to learn your
way around a library, don't read this book!
Find the library you wish
to learn, and work through the tutorials and documentation.
But be
warned.
While this has an immediate problem-solving payoff, if you
want to understand what's really going on in neural networks, if you
want insights that will still be relevant years from now, then it's
not enough just to learn some hot library.
You need to understand the
durable, lasting insights underlying how neural networks work.
Technologies come and technologies go, but insight is forever.We'll learn the core principles behind neural networks and deep
learning by attacking a concrete problem: the problem of teaching a
computer to recognize handwritten digits.
This problem is extremely
difficult to solve using the conventional approach to programming.
And yet, as we'll 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.
What's more, we'll 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 you'll need some programming
experience to read the book.
But you don't need to be a professional
programmer.
I've written the code in Python (version 2.7), which,
even if you don't program in Python, 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 experiment and
to build understanding.
All the code is available for download
.
Once you've finished the book, or as you read it, you can easily pick
up one of the more feature-complete neural network libraries 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 it's usually
just elementary algebra and plots of functions, which I expect most
readers will be okay with.
I 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 extensively is , which
requires a little multivariable calculus and linear algebra.
If those
aren't familiar, I begin
with a
discussion of how to navigate the mathematics.
If you're finding it
really heavy going, you can simply skip to the
of the
chapter's main results.
In any case, there's no need to worry about
this at the outset.It's rare for a book to aim to be both principle-oriented and
hands-on.
But I believe you'll learn best if we build out the
fundamental ideas of neural networks.
We'll develop living code, not
just abstract theory, code which you can explore and extend.
This way
you'll understand the fundamentals, both in theory and practice, and
be well set to add further to your knowledge.扫二维码下载作业帮
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What is most obvious in this book are all those details of daily living which make Mrs.Richard _____ common.A.nothing butB.anything butC.above allD.rather than
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答案Banything but.(="far" from being) 根本不; nothing but 只不过; above all最重要的.rather than 而不是"
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【题文】What is most obvious in this book are all those details of daily living whic
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【题文】What is most obvious in this book are all those details of daily living which make Mrs. Richard _____ common.
A．nothing but
B．anything but
C．above all
D．rather than
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【解析】anything but.(="far" from being) 根本不; nothing but 只不过; above all最重要的。rather than 而不是
【解析】anything but.(="far" from being) 根本不; nothing but 只不过; above all最重要的。rather than 而不是
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