Table Of ContentMachine Learning for
Developers
Uplift your regular applications with the power of statistics,
analytics, and machine learning
Rodolfo Bonnin
BIRMINGHAM - MUMBAI
Machine Learning for Developers
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First published: October 2017
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Published by Packt Publishing Ltd.
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ISBN 978-1-78646-987-8
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Credits
Author Copy Editor
Rodolfo Bonnin Safis Editing
Reviewers Project Coordinator
Doug Ortiz Nidhi Joshi
Md. Mahmudul Hasan
Commissioning Editor Proofreader
Veena Pagare Safis Editing
Acquisition Editor Indexer
Vinay Argekar Francy Puthiry
Content Development Editor Graphics
Aishwarya Pandere Tania Dutta
Technical Editor Production Coordinator
Karan Thakkar 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.
This exponentially growing set of knowledge, techniques, and programming libraries
makes most classical texts on the subject obsolete, at least for the deployment of fast and
practical applications. For this reason, a book like this can be celebrated as a quick and to-
the-point text that provides all the materials required to successfully implement and
understand a machine learning application in a single reading. In this book, you will find:
1. The fundamentals of machine learning tasks (classification, clustering, regression,
and data reduction), together with a quick, yet comprehensive introduction to the
mathematical and statistical foundations of the subject.
2. A more detailed presentation of Neural Networks as a learning model, together
with basics of the training algorithms, convergence crite0ria, and the evaluation
of results.
3. An introduction the most advanced learning models using more elaborate
networks, including convolutional, recurrent, and adversarial networks. Each of
the models is analyzed thoroughly, both in theoretical and in practical
considerations.
4. A comprehensive guide to open source software that, together with the previous
material, allows the reader to put the concepts into practice very quickly.
This book is highly recommended for practitioners in academia who feel their expertise is
becoming outdated, for developers who need to deploy sophisticated machine learning
features in business applications, and for anyone willing to gain a broad and practical
understanding of machine learning. The author transmits his vast experience in the subject
in a very clear and systematic manner, making the book easy to follow and put into
practice.
Claudio Delrieux
Full Professor at Electric and Computer Engineering Department - Universidad Nacional
del Sur
Fellow - National Research and Technology Council of Argentina
Chair - Imaging Sciences Laboratory
About the Author
Rodolfo Bonnin is a systems engineer and Ph.D. student at Universidad Tecnológica
Nacional, Argentina. He has also pursued parallel programming and image understanding
postgraduate courses at Universität Stuttgart, Germany.
He has been doing research on high-performance computing since 2005 and began studying
and implementing convolutional neural networks in 2008, writing a CPU- and GPU-
supporting neural network feedforward stage. More recently he's been working in the field
of fraud pattern detection with Neural Networks and is currently working on signal
classification using machine learning techniques.
He is also the author of Building Machine Learning Projects with Tensorflow, by Packt
Publishing.
About the Reviewers
Doug Ortiz is a Senior Big Data Architect at ByteCubed who has been architecting,
developing, and integrating enterprise solutions throughout his whole career.
Organizations that leverage his skillset have been able to rediscover and reuse their
underutilized data via existing and emerging technologies such as Amazon Web Services,
Microsoft Azure, Google Cloud, Microsoft BI stack, Hadoop, Spark, NoSQL databases,
SharePoint along with related toolsets and technologies.
He is also, the founder of Illustris, LLC and can be reached at [email protected]
Interesting aspects of his profession include:
Experience integrating multiple platforms and products
Has Big Data, Data Science Certifications, R, and Python Certifications
Helping organizations gain a deeper understanding and value of their current
investments in data and existing resources turning them into useful sources of
information
Improving, salvaging and architecting projects by utilizing unique and
innovative techniques
Reviews regularly books on the topics of Amazon Web Services, Data Science,
Machine Learning, R and Cloud Technologies
Hobbies:
Yoga
Scuba diving
I would like to thank my wonderful wife Mila for all her help and support as well as Maria
and Nikolay and our wonderful children.
Md. Mahmudul Hasan is currently serving as a Ph.D. researcher at Anglia Ruskin IT
Research Institute (ARITI) at Anglia Ruskin University, UK. He worked as a senior lecturer
in the department of CSE at Daffodil International University. He graduated from
University of Essex, UK and specialized in games and mobile apps development in different
platforms.
He has 6+ years of experiences in ICT industry including development to commercialize of
small and large software. He is currently serving himself as a country Director and Chair of
International Game Developers Association (IGDA) Bangladesh Chapter.
Mahmud's research interests include machine learning, data science, decision support
system, and personalized learning through games and gamification. He has significant
publications in peer-reviewed journals and conferences.
