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Jojo John Moolayil - Learn Keras for Deep Neural Networks: A Fast-Track Approach to Modern Deep Learning with Python

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Jojo John Moolayil Learn Keras for Deep Neural Networks: A Fast-Track Approach to Modern Deep Learning with Python
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Learn, understand, and implement deep neural networks in a math- and programming-friendly approach using Keras and Python. The book focuses on an end-to-end approach to developing supervised learning algorithms in regression and classification with practical business-centric use-cases implemented in Keras.

The overall book comprises three sections with two chapters in each section. The first section prepares you with all the necessary basics to get started in deep learning. Chapter 1 introduces you to the world of deep learning and its difference from machine learning, the choices of frameworks for deep learning, and the Keras ecosystem. You will cover a real-life business problem that can be solved by supervised learning algorithms with deep neural networks. Youll tackle one use case for regression and another for classification leveraging popular Kaggle datasets.

Later, you will see an interesting and challenging part of deep learning: hyperparameter tuning; helping you further improve your models when building robust deep learning applications. Finally, youll further hone your skills in deep learning and cover areas of active development and research in deep learning.

At the end of Learn Keras for Deep Neural Networks, you will have a thorough understanding of deep learning principles and have practical hands-on experience in developing enterprise-grade deep learning solutions in Keras.

What Youll Learn

  • Master fast-paced practical deep learning concepts with math- and programming-friendly abstractions.
  • Design, develop, train, validate, and deploy deep neural networks using the Keras framework
  • Use best practices for debugging and validating deep learning models
  • Deploy and integrate deep learning as a service into a larger software service or product
  • Extend deep learning principles into other popular frameworks

Who This Book Is For

Software engineers and data engineers with basic programming skills in any language and who are keen on exploring deep learning for a career move or an enterprise project.

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Jojo Moolayil Learn Keras for Deep Neural Networks A Fast-Track Approach to - photo 1
Jojo Moolayil
Learn Keras for Deep Neural Networks A Fast-Track Approach to Modern Deep Learning with Python
Jojo Moolayil Vancouver BC Canada Any source code or other supplementary - photo 2
Jojo Moolayil
Vancouver, BC, Canada

Any source code or other supplementary material referenced by the author in this book is available to readers on GitHub via the books product page, located at www.apress.com/978-1-4842-4239-1 . For more detailed information, please visit http://www.apress.com/source-code .

ISBN 978-1-4842-4239-1 e-ISBN 978-1-4842-4240-7
https://doi.org/10.1007/978-1-4842-4240-7
Library of Congress Control Number: 2018965596
Jojo Moolayil 2019
Standard Apress
Trademarked names, logos, and images may appear in this book. Rather than use a trademark symbol with every occurrence of a trademarked name, logo, or image we use the names, logos, and images only in an editorial fashion and to the benefit of the trademark owner, with no intention of infringement of the trademark. The use in this publication of trade names, trademarks, service marks, and similar terms, even if they are not identified as such, is not to be taken as an expression of opinion as to whether or not they are subject to proprietary rights.
While the advice and information in this book are believed to be true and accurate at the date of publication, neither the authors nor the editors nor the publisher can accept any legal responsibility for any errors or omissions that may be made. The publisher makes no warranty, express or implied, with respect to the material contained herein.
Distributed to the book trade worldwide by Springer Science+Business Media New York, 233 Spring Street, 6th Floor, New York, NY 10013. Phone 1-800-SPRINGER, fax (201) 348-4505, e-mail orders-ny@springer-sbm.com, or visit www.springeronline.com. Apress Media, LLC is a California LLC and the sole member (owner) is Springer Science + Business Media Finance Inc (SSBM Finance Inc). SSBM Finance Inc is a Delaware corporation.
Introduction

This book is intended to gear the readers with a superfast crash course on deep learning. Readers are expected to have basic programming skills in any modern-day language; Python experience would be great, but is not necessary. Given the limitations on the size and depth of the subject we can cover, this short guide is intended to equip you as a beginner with sound understanding of the topic, including tangible practical experience in model development that will help develop a foundation in the deep learning domain.

This guide is not recommended if you are already above the beginner level and are keen to explore advanced topics in deep learning like computer vision, speech recognition, and so on. The topics of CNN, RNN, and modern unsupervised learning algorithms are beyond the scope of this guide. We provide only a brief introduction to these to keep the readers aware contextually about more advanced topics and also provide recommended sources to explore these topics in more detail.

What will you learn from this guide?

The book is focused on a fast-paced approach to exploring practical deep learning concepts with math and programming-friendly abstractions. You will learn to design, develop, train, validate, and deploy deep neural networks using the industrys favorite Keras framework. You will also learn about the best practices for debugging and validating deep learning models and briefly learn about deploying and integrating deep learning as a service into a larger software service or product. Finally, with the experience gained in building deep learning models with Keras, you will also be able to extend the same principles into other popular frameworks.

Who is this book for?

The primary target audience for this book consists of software engineers and data engineers keen on exploring deep learning for a career move or an upcoming enterprise tech project. We understand the time crunch you may be under and the pain of assimilating new content to get started with the least amount of friction. Additionally, this book is for data science enthusiasts and academic and research professionals exploring deep learning as a tool for research and experiments.

What is the approach to learning in the book?

We follow the lazy programming approach in this guide. We start with a basic introduction, and then cater to the required context incrementally at each step. We discuss how each building block functions in a lucid way and then learn about the abstractions available to implement them.

How is the book structured?

The book is organized into three sections with two chapters each.

Section 1 equips you with all the necessary gear to get started on the fast-track ride into deep learning. Chapter will help you get started with a hands-on exercise in Keras, understanding the basic building blocks of deep learning and developing the first basic DNN.

Section 2 embraces the fundamentals of deep learning in simple, lucid language while abstracting the math and complexities of model training and validation with the least amount of code without compromising on flexibility, scale, and the required sophistication. Chapter delves into the craft of validating deep neural networks (i.e., measuring performance and understanding the shortcomings and the means to circumvent them).

Section 3 concludes the book with topics on further model improvement and the path forward. Chapter the conclusiondiscusses the path ahead for the reader to further hone his or her skills in deep learning and discusses a few areas of active development and research in deep learning.

At the end of this crash course, the reader will have gained a thorough understanding of the deep learning principles within the shortest possible time frame and will have obtained practical hands-on experience in developing enterprise-grade deep learning solutions in Keras.

Acknowledgments

I would like to thank my parents, my brother Tijo, and my sister Josna for their constant support and love.

Table of Contents
About the Author and About the Technical Reviewer
About the Author
Jojo Moolayil
is an artificial intelligence deep learning machine learning and decision - photo 3

is an artificial intelligence, deep learning, machine learning, and decision science professional and the author of the book Smarter Decisions: The Intersection of IoT and Decision Science (Packt, 2016). He has worked with industry leaders on several high-impact and critical data science and machine learning projects across multiple verticals. He is currently associated with Amazon Web Services as a Research ScientistAI.

Jojo was born and raised in Pune, India and graduated from the University of Pune with a major in Information Technology Engineering. He started his career with Mu Sigma Inc., the worlds largest pure-play analytics provider, and worked with the leaders of many Fortune 50 clients. He later worked with Flutura, an IoT analytics startup, and GE, the pioneer and leader in industrial AI.

He currently resides in Vancouver, BC. Apart from authoring books on deep learning, decision science, and IoT, Jojo has also been technical reviewer for various books on the same subject with Apress and Packt Publishing. He is an active data science tutor and maintains a blog at http://blog.jojomoolayil.com .

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