Java Deep Learning Essentials

Yusuke Sugomori

• Publisher: Packt Publishing
• Year: 2016
• eBook ISBN: 9781785282195
• Edition: 1
• Topic: Informatica
• Subtopic: Intelligenza artificiale (IA) e semantica

Dive into the future of data science and learn how to build the sophisticated algorithms that are fundamental to deep learning and AI with Java

About This Book

• Go beyond the theory and put Deep Learning into practice with Java
• Find out how to build a range of Deep Learning algorithms using a range of leading frameworks including DL4J, Theano and Caffe
• Whether you're a data scientist or Java developer, dive in and find out how to tackle Deep Learning

Who This Book Is For

This book is intended for data scientists and Java developers who want to dive into the exciting world of deep learning. It would also be good for machine learning users who intend to leverage deep learning in their projects, working within a big data environment.

What You Will Learn

• Get a practical deep dive into machine learning and deep learning algorithms
• Implement machine learning algorithms related to deep learning
• Explore neural networks using some of the most popular Deep Learning frameworks
• Dive into Deep Belief Nets and Stacked Denoising Autoencoders algorithms
• Discover more deep learning algorithms with Dropout and Convolutional Neural Networks
• Gain an insight into the deep learning library DL4J and its practical uses
• Get to know device strategies to use deep learning algorithms and libraries in the real world
• Explore deep learning further with Theano and Caffe

In Detail

AI and Deep Learning are transforming the way we understand software, making computers more intelligent than we could even imagine just a decade ago. Deep Learning algorithms are being used across a broad range of industries – as the fundamental driver of AI, being able to tackle Deep Learning is going to a vital and valuable skill not only within the tech world but also for the wider global economy that depends upon knowledge and insight for growth and success. It's something that's moving beyond the realm of data science – if you're a Java developer, this book gives you a great opportunity to expand your skillset.

Starting with an introduction to basic machine learning algorithms, to give you a solid foundation, Deep Learning with Java takes you further into this vital world of stunning predictive insights and remarkable machine intelligence. Once you've got to grips with the fundamental mathematical principles, you'll start exploring neural networks and identify how to tackle challenges in large networks using advanced algorithms. You will learn how to use the DL4J library and apply Deep Learning to a range of real-world use cases. Featuring further guidance and insights to help you solve challenging problems in image processing, speech recognition, language modeling, this book will make you rethink what you can do with Java, showing you how to use it for truly cutting-edge predictive insights. As a bonus, you'll also be able to get to grips with Theano and Caffe, two of the most important tools in Deep Learning today.

By the end of the book, you'll be ready to tackle Deep Learning with Java. Wherever you've come from – whether you're a data scientist or Java developer – you will become a part of the Deep Learning revolution!

Style and approach

This is a step-by-step, practical tutorial that discusses key concepts. This book offers a hands-on approach to key algorithms to help you develop a greater understanding of deep learning. It is packed with implementations from scratch, with detailed explanation that make the concepts easy to understand and follow.

Java Deep Learning Essentials

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Table of Contents

Java Deep Learning Essentials

Credits

About the Author

About the Reviewers

www.PacktPub.com

eBooks, discount offers, and more

Why subscribe?

Preface

What this book covers

What you need for this book

Who this book is for

Conventions

Reader feedback

Customer support

Downloading the example code

Errata

Piracy

Questions

1. Deep Learning Overview

Transition of AI

Definition of AI

AI booms in the past

Machine learning evolves

What even machine learning cannot do

Things dividing a machine and human

AI and deep learning

Summary

2. Algorithms for Machine Learning – Preparing for Deep Learning

Getting started

The need for training in machine learning

Supervised and unsupervised learning

Support Vector Machine (SVM)

Hidden Markov Model (HMM)

Neural networks

Logistic regression

Reinforcement learning

Machine learning application flow

Theories and algorithms of neural networks

Perceptrons (single-layer neural networks)

Logistic regression

Multi-class logistic regression

Multi-layer perceptrons (multi-layer neural networks)

Summary

3. Deep Belief Nets and Stacked Denoising Autoencoders

Neural networks fall

Neural networks' revenge

Deep learning's evolution – what was the breakthrough?

Deep learning with pre-training

Deep learning algorithms

Restricted Boltzmann machines

Deep Belief Nets (DBNs)

Denoising Autoencoders

Stacked Denoising Autoencoders (SDA)

Summary

4. Dropout and Convolutional Neural Networks

Deep learning algorithms without pre-training

Dropout

Convolutional neural networks

Convolution

Pooling

Equations and implementations

Summary

5. Exploring Java Deep Learning Libraries – DL4J, ND4J, and More

Implementing from scratch versus a library/framework

Introducing DL4J and ND4J

Implementations with ND4J

Implementations with DL4J

Setup

Build

DBNIrisExample.java

CSVExample.java

CNNMnistExample.java/LenetMnistExample.java

Learning rate optimization

Summary

6. Approaches to Practical Applications – Recurrent Neural Networks and More

Fields where deep learning is active

Image recognition

Natural language processing

Feed-forward neural networks for NLP

Deep learning for NLP

Recurrent neural networks

Long short term memory networks

The difficulties of deep learning

The approaches to maximizing deep learning possibilities and abilities

Field-oriented approach

Medicine

Automobiles

Advert technologies

Profession or practice

Sports

Breakdown-oriented approach

Output-oriented approach

Summary

7. Other Important Deep Learning Libraries

Theano

TensorFlow

Caffe

Summary

8. What's Next?

Breaking news about deep learning

Expected next actions

Useful news sources for deep learning

Summary

Index

Java Deep Learning Essentials

Copyright © 2016 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.

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First published: May 2016

Production reference: 1250516

Published by Packt Publishing Ltd.

Livery Place

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Birmingham B3 2PB, UK.

ISBN 978-1-78528-219-5

www.packtpub.com

Credits

Author

Yusuke Sugomori

Reviewers

Wei Di

Vikram Kalabi

Commissioning Editor

Kartikey Pandey

Acquisition Editor

Manish Nainani

Content Development Editor

Rohit Singh

Technical Editor

Vivek Arora

Copy Editor

Ameesha Smith Green

Project Coordinator

Izzat Contractor

Proofreader

Safis Editing

Indexer

Mariammal Chettiyar

Graphics

Abhinash Sahu

Production Coordinator

Arvindkumar Gupta

Cover Work

Arvindkumar Gupta

About the Author

Yusuke Sugomori is a creative technologist with a background in information engineering. When he was a graduate school student, he cofounded Gunosy with his colleagues, which uses machine learning and web-based data mining to determine individual users' respective interests and provides an optimized selection of daily news items based on those interests. This algorithm-based app has gained a lot of attention since its release and now has more than 10 million users. The company has been listed on the Tokyo Stock Exchange since April 28, 2015.

In 2013, Sugomori joined Dentsu, the largest advertising company in Japan based on nonconsolidated gross profit in 2014, where he carried out a wide variety of digital advertising, smartphone app development, and big data analysis. He was also featured as one of eight "new generation" creators by the Japanese magazine Web Designing.

In April 2016, he joined a medical start-up as cofounder and CTO.

About the Reviewers

Wei Di is a data scientist. She is passionate about creating smart and scalable analytics and data mining solutions that can impact millions of individuals and empower successful businesses.

Her interests also cover wide areas including artificial intelligence, machine learning, and computer vision. She was previously associated with the eBay Human Language Technology team and eBay Research Labs, with a focus on image understanding for large scale applications and joint learning from both visual and text information. Prior to that, she was with Ancestry.com working on large-scale data mining and machine learning models in the areas of record linkage, search relevance, and ranking. She received her PhD from Purdue University in 2011 with focuses on data mining and image classification.

Vikram Kalabi is a data scientist. He is working on a Cognitive System that can enable smart plant breeding. His work is primarily in predictive analytics and mathematical optimization. He has also worked on large scale data-driven decision making systems with a focus on recommender systems. He is excited about data science that can help improve farmer's life and help reduce food scarcity in the world. He is a certified data scientist from John Hopkins University.

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Table of contents

• Java Deep Learning Essentials