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Machine Learning for Developers 无水印原版pdf
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Machine Learning for
Developers
Uplift your regular applications with the power of statistics,
analytics, and machine learning
Rodolfo Bonnin
BIRMINGHAM - MUMBAI
Machine Learning for Developers
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 author, 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: October 2017
Production reference: 1241017
Published by Packt Publishing Ltd.
Livery Place
35 Livery Street
Birmingham
B3 2PB, UK.
ISBN 978-1-78646-987-8
www.packtpub.com
Credits
Author
Rodolfo Bonnin
Copy Editor
Safis Editing
Reviewers
Doug Ortiz
Md. Mahmudul Hasan
Project Coordinator
Nidhi Joshi
Commissioning Editor
Veena Pagare
Proofreader
Safis Editing
Acquisition Editor
Vinay Argekar
Indexer
Francy Puthiry
Content Development Editor
Aishwarya Pandere
Graphics
Tania Dutta
Technical Editor
Karan Thakkar
Production Coordinator
Shraddha Falebhai
Foreword
Several technological drivers that arose in the last decade that I made big data possible,
irreversibly reshaping the world completely. Among these, machine learning plays a
singular role, since it provides the main functionalities required for data analysis, mining,
knowledge discovery, and many other features that provide actionable autonomous
intelligence in a way invisible but pervasive to most of the systems we use in our daily
lives. Although not new, the formalisms and methods within machine learning have
quickly evolved, driven by the growing demands of e-commerce, social networks, internet-
related services and products, and similar enterprises centered on online business.
Breakthroughs in machine learning have been fueled by other technological innovations
sprouted and matured within the Hadoop ecosystem, including horizontally scalable
computational resources and superior warehousing capabilities that have made the real-
time analysis of huge datasets feasible. At the same time, community supported initiatives
around the Python programming language have made the use and evolution of
sophisticated analysis libraries widspread, giving rise to a remarkable amount of
knowledge and experience, at the same time fast and easy to deploy and to put into
production.
Within machine learning, neural networks play a singular role nowadays. Perhaps the first
artificial intelligence paradigm to be proposed more than 70 years ago, neural networks
have experienced several cycles of being abandoned by the community only to be
rediscovered some years later. This was likely due to the lack of computational power to
perform really complex analysis adequately, together with the burdensome task of
assembling, training, and testing different topologies by trial and error. This has changed
dramatically in recent years, mostly due to the availability of cloud computing, GPUs, and
programming libraries that allow the set up of networks with simple scripts. Today, a
network with hundreds of millions of degrees of freedom can be assembled in minutes,
trained in hours, and put into production in a few days, (obviously, if you know the right
technologies to so). This is one of the reasons why most of the radical advancements in
computer vision, language understanding, and pattern recognition in general are being
driven specifically by different flavors of neural networks that have been proposed recently.
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