Java Deep Learning Essentials

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

Java Deep Learning Essentials

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Yusuke Sugomori

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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
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Book Details

ISBN 139781785282195
Paperback254 pages

Book Description

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!

Table of Contents

Chapter 1: Deep Learning Overview
Transition of AI
Things dividing a machine and human
AI and deep learning
Summary
Chapter 2: Algorithms for Machine Learning – Preparing for Deep Learning
Getting started
The need for training in machine learning
Supervised and unsupervised learning
Machine learning application flow
Theories and algorithms of neural networks
Summary
Chapter 3: Deep Belief Nets and Stacked Denoising Autoencoders
Neural networks fall
Neural networks' revenge
Deep learning algorithms
Summary
Chapter 4: Dropout and Convolutional Neural Networks
Deep learning algorithms without pre-training
Dropout
Convolutional neural networks
Summary
Chapter 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
Summary
Chapter 6: Approaches to Practical Applications – Recurrent Neural Networks and More
Fields where deep learning is active
The difficulties of deep learning
The approaches to maximizing deep learning possibilities and abilities
Summary
Chapter 7: Other Important Deep Learning Libraries
Theano
TensorFlow
Caffe
Summary
Chapter 8: What's Next?
Breaking news about deep learning
Expected next actions
Useful news sources for deep learning
Summary

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

Authors

Table of Contents

Chapter 1: Deep Learning Overview
Transition of AI
Things dividing a machine and human
AI and deep learning
Summary
Chapter 2: Algorithms for Machine Learning – Preparing for Deep Learning
Getting started
The need for training in machine learning
Supervised and unsupervised learning
Machine learning application flow
Theories and algorithms of neural networks
Summary
Chapter 3: Deep Belief Nets and Stacked Denoising Autoencoders
Neural networks fall
Neural networks' revenge
Deep learning algorithms
Summary
Chapter 4: Dropout and Convolutional Neural Networks
Deep learning algorithms without pre-training
Dropout
Convolutional neural networks
Summary
Chapter 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
Summary
Chapter 6: Approaches to Practical Applications – Recurrent Neural Networks and More
Fields where deep learning is active
The difficulties of deep learning
The approaches to maximizing deep learning possibilities and abilities
Summary
Chapter 7: Other Important Deep Learning Libraries
Theano
TensorFlow
Caffe
Summary
Chapter 8: What's Next?
Breaking news about deep learning
Expected next actions
Useful news sources for deep learning
Summary

Book Details

ISBN 139781785282195
Paperback254 pages
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