Table Of ContentPredictive Analytics with
TensorFlow
Implement deep learning principles to predict valuable
insights using TensorFlow
Md. Rezaul Karim
BIRMINGHAM - MUMBAI
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Predictive Analytics with TensorFlow
Copyright © 2017 Packt Publishing
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First published: October 2017
Production reference: 1251017
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ISBN 978-1-78839-892-3
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Credits
Author Project Coordinator
Md. Rezaul Karim Shweta H Birwatkar
Reviewers Proofreader
Andrea Mostosi Safis Editing
Meng-Chieh Ling
Indexer
Pratik Shirodkar
Commissioning Editor
Sunith Shetty
Graphics
Tania Dutta
Acquisition Editor
Chandan Kumar
Production Coordinator
Aparna Bhagat
Content Development Editor
Amrita Noronha
Technical Editor
Sayali Thanekar
Copy Editor
Safis Editing
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About the Author
Md. Rezaul Karim is a Research Scientist at Fraunhofer FIT, Germany. He is also
a PhD candidate at RWTH Aachen University, Aachen, Germany. He holds a BSc
and an MSc degree in Computer Science. Before joining Fraunhofer FIT, he worked
as a Researcher at Insight Centre for Data Analytics, Ireland. Before this, he worked
as a Lead Engineer at Samsung Electronics' distributed R&D Institutes in Korea,
India, Turkey, and Bangladesh. Previously, he has worked as a Research Assistant at
the database lab, Kyung Hee University, Korea. He also worked as an R&D engineer
with BMTech21 Worldwide, Korea. Even before this, he worked as a Software
Engineer with i2SoftTechnology, Dhaka, Bangladesh.
He has more than 8 years of experience in the area of research and development with
solid understanding of algorithms and data structures in C, C++, Java, Scala, R, and
Python. He has published several books, articles, and research papers concerning
big data and virtualization technologies, such as Spark, Kafka, DC/OS, Docker,
Mesos, Zeppelin, Hadoop, and MapReduce. He is also equally competent with deep
learning technologies such as TensorFlow, DeepLearning4j, and H2O. His research
interests include Machine Learning, Deep Learning, Semantic Web, Linked Data,
Big Data, and Bioinformatics. Also, he is the author of the following book titles:
• Large-Scale Machine Learning with Spark (Packt Publishing Ltd.)
• Deep Learning with TensorFlow (Packt Publishing Ltd.)
• Scala and Spark for Big Data Analytics (Packt Publishing Ltd.)
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Acknowledgments
I am very grateful to my parents, who have always encouraged me to pursue
knowledge. I also want to thank my wife, Saroar; son, Shadman; brother, Mamtaz;
sister, Josna; and friends who have endured my long monologs about the subjects
in this book and always have encouraged and listened to me. Writing this book
was made easier by the amazing efforts of the open source community and the
great documentation of many projects out there related to TensorFlow and Python.
Further, I would like to thank the acquisition, content development, and technical
editors of Packt Publishing Ltd. (and, of course, others who were involved in this
book title) for their sincere cooperation and coordination. Additionally, without the
work of numerous researchers and deep learning practitioners who shared their
expertise in publications, lectures, and source code, this book might not have existed
at all! Finally, I appreciate the efforts of the TensorFlow community and all those
who have contributed to APIs, whose work ultimately brought machine learning
to the masses.
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About the Reviewers
Andrea Mostosi is a technology enthusiast, a husband, and a father. During
the last 10 years, he led the entire life cycle of several projects across different
technologies, companies, and markets. He is now working on artificial intelligence,
data mining, and a lot of other scary things.
I'd like to thank my wonderful son, Ryan, for every smile, every
hug, and every sleepless night he gave me since his birth. When the
machines finally take over humanity, you'll be able to say that your
father has contributed to making this happen, my son.
Meng-Chieh Ling is a theoretical physics PhD from Karlsruhe Institute of
Technology. After finishing his PhD, he attended The Data Incubator Reply to
change his career from theoretical physics to data science.
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Table of Contents
Preface ix
Chapter 1: Basic Python and Linear Algebra for
Predictive Analytics 1
A basic introduction to predictive analytics 2
Why predictive analytics? 3
Working principles of a predictive model 3
A bit of linear algebra 7
Programming linear algebra 8
Installing and getting started with Python 9
Installing on Windows 9
Installing Python on Linux 11
Installing and upgrading PIP (or PIP3) 12
Installing Python on Mac OS 12
Installing packages in Python 13
Getting started with Python 13
Python data types 14
Using strings in Python 14
Using lists in Python 15
Using tuples in Python 16
Using dictionary in Python 17
Using sets in Python 18
Functions in Python 19
Classes in Python 20
Vectors, matrices, and graphs 21
Vectors 21
Matrices 25
Matrix addition 25
Matrix subtraction 25
Finding the determinant of a matrix 27
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