MEAP Edition
Manning Early Access Program
Machine Learning with TensorFlow
Version 10
Copyright 2017 Manning Publications
For more information on this and other Manning titles go to
www.manning.com
©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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welcome
Dear fellow early adopters, curious readers, and puzzled newcomers,
Thank you all for every bit of communication with me, whether it be through the official
book forums, through email, on GitHub, or even on Reddit. I’ve listened carefully to your
questions, suggestions, and concerns, regardless of whether or not I’ve replied to you (and I
do apologize for not replying to you).
In the latest edition, I am proud to announce a beautiful makeover of every chapter. The
text is greatly improved and slowed down to better cover complex matters, especially the
areas where you requested more explanation. Most figures and mathematical equations have
been updated to look crisp and professional. The code is now updated to TensorFlow v1.0, and
it is also available on GitHub at https://github.com/BinRoot/TensorFlow-Book/
. Also, the
chapters are rearranged to better deliver the right skills at the right time, if the book were
read in order.
Thank you for investing in the MEAP edition of Machine Learning with TensorFlow. You’re
one of the first to dive into this introductory book about cutting-edge machine learning
techniques using the hottest technology (spoiler alert: I’m talking about TensorFlow). You’re a
brave one, dear reader. And for that, I reward you generously with the following.
You’re about to learn machine learning from scratch, both the theory and how to easily
implement it. As long as you roughly understand object-oriented programming and know how
to use Python, this book will teach you everything you need to know to start solving your own
big-data problems, whether it be for work or research.
TensorFlow was released just over a year ago by some company that specializes in search
engine technology. Okay, I’m being a little facetious; well-known researchers at Google
engineered this library. But with such prowess comes intimidating documentation and
assumed knowledge. Fortunately for you, this book is down-to-earth and greets you with open
arms.
Each chapter zooms into a prominent example of machine learning, such as classification,
regression, anomaly detection, clustering, and many modern neural networks. Cover them all
to master the basics, or cater it to your needs by skipping around.
Keep me updated on typos, mistakes, and improvements because this book is undergoing
heavy development. It’s like living in a house that’s still actively under construction; at least
you won’t have to pay rent. But on a serious note, your feedback along the way will be
appreciated.
With gratitude,
—Nishant Shukla
©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.
https://forums.manning.com/forums/machine-learning-with-tensorflow
brief contents
P
ART
1
M
Y
M
ACHINE
L
EARNING
R
IG
1 A machine learning odyssey
2 TensorFlow essentials
P
ART
2
C
ORE
L
EARNING
A
LGORITHMS
3 Linear regression and beyond
4 A gentle introduction to classification
5 Automatically clustering data
6 Hidden Markov models
P
ART
3
T
HE
N
EURAL
N
ETWORK
P
ARADIGM
7 A peek into autoencoders
8 Reinforcement learning
9 Convolutional neural networks
10 Recurrent neural networks
11 Sequence-to-sequence models for chatbots
12 Utility landscape
A
PPENDIX
A Installation
©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.
https://forums.manning.com/forums/machine-learning-with-tensorflow
1
A machine-learning odyssey
This chapter covers
• Machine learning fundamentals
• Data representation, features, and vector norms
• Existing machine learning tools
• Why TensorFlow
Have you ever wondered if there are limits to what computer programs can solve?
Nowadays, computers appear to do a lot more than simply unravel mathematical equations. In
the last half-century, programming has become the ultimate tool to automate tasks and save
time, but how much can we automate, and how do we go about doing so?
1
