MEAP Edition
Manning Early Access Program
Deep Learning with Python
Version 6
Copyright 2017 Manning Publications
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Welcome
Thank you for purchasing the MEAP for
Deep Learning with Python.
If you are looking
for a resource to learn about deep learning from scratch and to quickly become able to use
this knowledge to solve real-world problems, you have found the right book. *Deep
Learning with Python* is meant for engineers and students with a reasonable amount of
Python experience, but no significant knowledge of machine learning and deep learning. It
will take you all the way from basic theory to advanced practical applications. However, if
you already have experience with deep learning, you should still be able to find value in
the latter chapters of this book.
Deep learning is an immensely rich subfield of machine learning, with powerful
applications ranging from machine perception to natural language processing, all the way up
to creative AI. Yet, its core concepts are in fact very simple. Deep learning is often
presented as shrouded in a certain mystique, with references to algorithms that “work like
the brain”, that “think” or “understand”. Reality is however quite far from this science-
fiction dream, and I will do my best in these pages to dispel these illusions. I believe that
there are no difficult ideas in deep learning, and that’s why I started this book, based on
premise that all of the important concepts and applications in this field could be taught to
anyone, with very few prerequisites.
This book is structured around a series of practical code examples, demonstrating on real-
world problems every the notions that gets introduced. I strongly believe in the value of
teaching using concrete examples, anchoring theoretical ideas into actual results and
tangible code patterns. These examples all rely on Keras, the Python deep learning library.
When I released the initial version of Keras almost two years ago, little did I know that it
would quickly skyrocket to become one of the most widely used deep learning frameworks.
A big part of that success is that Keras has always put ease of use and accessibility front and
center. This same reason is what makes Keras a great library to get started with deep
learning, and thus a great fit for this book. By the time you reach the end of this book, you
will have become a Keras expert.
I hope that you will this book valuable —deep learning will definitely open up new
intellectual perspectives for you, and in fact it even has the potential to transform your
career, being the most in-demand scientific specialization these days. I am looking forward
to your reviews and comments. Your feedback is essential in order to write the best possible
book, that will benefit the greatest number of people.
— François Chollet
©Manning Publications Co. We welcome reader comments about anything in the manuscript - other than typos and
other simple mistakes. These will be cleaned up during production of the book by copyeditors and proofreaders.
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brief contents
P
ART
1: T
HE FUNDAMENTALS OF
D
EEP
L
EARNING
1 What is Deep Learning?
2 Before we start: the mathematical building blocks of neural networks
3 Getting started with neural networks
4 Fundamentals of machine learning
P
ART
2: D
EEP LEARNING IN PRACTICE
5 Deep learning for computer vision
6 Deep learning for text and sequences
7 Advanced deep learning best practices
8 Generative deep learning
9 Conclusions
©Manning Publications Co. We welcome reader comments about anything in the manuscript - other than typos and
other simple mistakes. These will be cleaned up during production of the book by copyeditors and proofreaders.
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1
What is Deep Learning?
1
1.1 Artificial intelligence, machine learning and deep learning
In the past few years, Artificial Intelligence (AI) has been a subject of intense media
hype. Machine learning, deep learning, and AI come up in countless articles, often
outside of technology-minded publications. We are being promised a future of intelligent
chatbots, self-driving cars, and virtual assistants—a future sometimes painted in a grim
light, and sometimes as an utopia, where human jobs would be scarce and most economic
activity would be handled by robots or AI agents.
As a future or current practitioner of machine learning, it is important to be able to
recognize the signal in the noise, to tell apart world-changing developments from what
are merely over-hyped press releases. What is at stake is our future, and it is a future in
which you have an active role to play: after reading this book, you will be part of those
who develop the AIs. So let’s tackle these questions—what has deep learning really
achieved so far? How significant is it? Where are we headed next? Should you believe
the hype?
First of all, we need to define clearly what we are talking about when we talk about
AI. What is artificial intelligence, machine learning, and deep learning? How do they
relate to each other?
Figure 1.1 Artificial Intelligence, Machine Learning and Deep Learning
©Manning Publications Co. We welcome reader comments about anything in the manuscript - other than typos and
other simple mistakes. These will be cleaned up during production of the book by copyeditors and proofreaders.
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