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Machine Learning with TensorFlow 1.x-Packt Publishing(2017).pdf
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Machine Learning has revolutionized the modern world. Many machine learning algorithms, especially deep learning, have been used worldwide, ranging from mobile devices to cloud-based services. TensorFlow is one of the leading open source software libraries and helps you build, train, and deploy your Machine Learning system for a variety of applications. This practical book is designed to bring you the best of TensorFlow and help you build real-world Machine Learning systems. By the end of this book, you will have a deep understanding of TensorFlow and be able to apply Machine Learning techniques to your application.
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Machine Learning with TensorFlow 1.x
Google’s TensorFlow is a game changer in
the world of machine learning. It has made
machine learning faster, simpler, and more
accessible than ever before. This book will
teach you how to easily get started with
machine learning using the power of Python
and TensorFlow 1.x.
Firstly, you’ll cover the basic installation
procedure and explore the capabilities
of TensorFlow 1.x. This is followed by
training and running the fi rst classifi er,
and coverage of the unique features of the
library including data fl ow graphs, training,
and the visualization of performance with
TensorBoard—all within an example-rich
context using problems from multiple
industries. You’ll be able to further explore
text and image analysis, and be introduced
to CNN models and their setup in
TensorFlow 1.x. Next, you’ll implement a
complete real-life production system from
training to serving a deep learning model. As
you advance you’ll learn about Amazon Web
Services (AWS) and create a deep neural
network to solve a video action recognition
problem. Lastly, you’ll convert the Caffe
model to TensorFlow and be introduced
to the high-level TensorFlow library,
TensorFlow-Slim.
By the end of this book, you will be geared
up to take on any challenges of implementing
TensorFlow 1.x in your machine learning
environment.
Things you will learn:
• Explore how to use different machine
learning models to ask different
questions of your data
• Learn how to build deep neural
networks using TensorFlow 1.x
• Cover key tasks such as clustering,
sentiment analysis, and regression
analysis using TensorFlow 1.x
• Find out how to write clean and
elegant Python code that will optimize
the strength of your algorithms
• Discover how to embed your machine
learning model in a web application
for increased accessibility
• Learn how to use multiple GPUs for
faster training using AWS
www.packtpub.com
Machine Learning with TensorFlow 1.x
Quan Hua, Shams Ul Azeem, Saif Ahmed
Second generation machine learning with Google’s
brainchild – TensorFlow 1.x
Machine
Learning with
TensorFlow 1.x
Quan Hua, Shams Ul Azeem, Saif Ahmed
Machine Learning with
TensorFlow 1.x
Second generation machine learning with Google's brainchild
- TensorFlow 1.x
Quan Hua
Shams Ul Azeem
Saif Ahmed
BIRMINGHAM - MUMBAI
Machine Learning with TensorFlow 1.x
Copyright © 2017 Packt Publishing
All rights reserved. No part of this book may be reproduced, stored in a retrieval system, or
transmitted in any form or by any means, without the prior written permission of the
publisher, except in the case of brief quotations embedded in critical articles or reviews.
Every effort has been made in the preparation of this book to ensure the accuracy of the
information presented. However, the information contained in this book is sold without
warranty, either express or implied. Neither the authors, nor Packt Publishing, and its
dealers and distributors will be held liable for any damages caused or alleged to be caused
directly or indirectly by this book.
Packt Publishing has endeavored to provide trademark information about all of the
companies and products mentioned in this book by the appropriate use of capitals.
However, Packt Publishing cannot guarantee the accuracy of this information.
First published: November 2017
Production reference: 1171117
Published by Packt Publishing Ltd.
Livery Place
35 Livery Street
Birmingham
B3 2PB, UK.
ISBN 978-1-78646-296-1
www.packtpub.com
Credits
Authors
Quan Hua
Shams Ul Azeem
Saif Ahmed
Copy Editor
Zainab Bootwala
Reviewer
Nathan Lintz
Project Coordinator
Prajakta Naik
Commissioning Editor
Kunal Parikh
Proofreader
Safis Editing
Acquisition Editor
Tushar Gupta
Indexer
Rekha Nair
Content Development Editor
Siddhi Chavan
Graphics
Jason Monteiro
Technical Editor
Mehul Singh
Production Coordinator
Deepika Naik
About the Authors
Quan Hua is a Computer Vision and Machine Learning Engineer at BodiData, a data
platform for body measurements, where he focuses on developing computer vision and
machine learning applications for a handheld technology capable of acquiring a body
avatar while a person is fully clothed. He earned a bachelor of science degree from the
University of Science, Vietnam, specializing in Computer Vision. He has been working in
the field of computer vision and machine learning for about 3 years at start-ups.
Quan has been writing for Packt since 2015 for a Computer Vision book, OpenCV 3
Blueprints.
I wish to thank everyone who has encouraged me on the way while writing this book.
I want to express my sincere gratitude to my co-authors, editors, and reviewers for their
advice and assistance.
I would like to thank the members of my family and my wife, Kim Ngoc, who supported
and encouraged me in spite of all the time it took me away from them. They all kept me
going, and this book would not have been possible without them.
I would also like to thank my teachers who gave me knowledge of Computer Vision and
Machine Learning.
Shams Ul Azeem is an undergraduate in electrical engineering from NUST Islamabad,
Pakistan. He has a great interest in the computer science field, and he started his journey
with Android development. Now, he’s pursuing his career in Machine Learning,
particularly in deep learning, by doing medical-related freelancing projects with different
companies.
He was also a member of the RISE lab, NUST, and he has a publication credit at the IEEE
International Conference, ROBIO as a co-author of Designing of motions for humanoid
goalkeeper robots.
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