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- Herausgeber: Packt Publishing
- Kategorie: Wissenschaft und neue Technologien
- Sprache: Englisch
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.
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Seitenzahl: 290
Veröffentlichungsjahr: 2016
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Table of Contents
Java Deep Learning Essentials
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.
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: May 2016
Production reference: 1250516
Published by Packt Publishing Ltd.
Livery Place
35 Livery Street
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.
www.PacktPub.com
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Preface
With an increasing interest in AI around the world, deep learning has attracted a great deal of public attention. Every day, deep learning algorithms are used across different industries. Deep learning has provided a revolutionary step to actualize AI. While it is a revolutionary technique, deep learning is often thought to be complicated, and so it is often kept from much being known of its contents. Theories and concepts based on deep learning are not complex or difficult. In this book, we'll take a step-by-step approach to learn theories and equations for the correct understanding of deep learning. You will find implementations from scratch, with detailed explanations of the cautionary notes for practical use cases.
What this book covers
Chapter 1, Deep Learning Overview, explores how deep learning has evolved.
Chapter 2, Algorithms for Machine Learning - Preparing for Deep Learning, implements machine learning algorithms related to deep learning.
Chapter 3, Deep Belief Nets and Stacked Denoising Autoencoders, dives into Deep Belief Nets and Stacked Denoising Autoencoders algorithms.
Chapter 4, Dropout and Convolutional Neural Networks, discovers more deep learning algorithms with Dropout and Convolutional Neural Networks.
Chapter 5, Exploring Java Deep Learning Libraries – DL4J, ND4J, and More, gains an insight into the deep learning library, DL4J, and its practical uses.
Chapter 6, Approaches to Practical Applications – Recurrent Neural Networks and More, lets you devise strategies to use deep learning algorithms and libraries in the real world.
Chapter 7, Other Important Deep Learning Libraries, explores deep learning further with Theano, TensorFlow, and Caffe.
Chapter 8, What's Next?, explores recent deep learning movements and events, and looks into useful deep learning resources.
What you need for this book
We'll implement deep learning algorithms using Lambda Expressions, hence Java 8 or above is required. Also, we'll use the Java library DeepLearning4J 0.4 or above.
Who this book is for
This book is for Java developers who want to know about deep learning algorithms and wish to implement them in applications.
Since this book covers the core concepts of and approaches to both machine learning and deep learning, no previous experience in machine learning is required.
Also, we will implement deep learning algorithms with very simple codes, so elementary Java developers will also find this book useful for developing both their Java skills and deep learning skills.
Conventions
In this book, you will find a number of text styles that distinguish between different kinds of information. Here are some examples of these styles and an explanation of their meaning.
Code words in text, database table names, folder names, filenames, file extensions, pathnames, dummy URLs, user input, and Twitter handles are shown as follows: "Let's take a look at CNNMnistExample.java in the package of convolution."
A block of code is set as follows:
Any command-line input or output is written as follows:
New terms and important words are shown in bold. Words that you see on the screen, for example, in menus or dialog boxes, appear in the text like this: "If you're using IntelliJ, you can import the project from File | New | Project from existing sources."
Note
Warnings or important notes appear in a box like this.
Tip
Tips and tricks appear like this.
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