Book Image

Java Deep Learning Essentials

By : Yusuke Sugomori
Book Image

Java Deep Learning Essentials

By: Yusuke Sugomori

Overview of this book

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 (15 chapters)
Java Deep Learning Essentials
About the Author
About the Reviewers
Other Important Deep Learning Libraries

Theories and algorithms of neural networks

In the previous section, you saw the general flow of when we perform data analysis with machine learning. In this section, theories and algorithms of neural networks, one of the methods of machine learning, are introduced as a preparation toward deep learning.

Although we simply say "neural networks", their history is long. The first published algorithm of neural networks was called perceptron, and the paper released in 1957 by Frank Rosenblatt was named The Perceptron: A Perceiving and Recognizing Automaton (Project Para). From then on, many methods were researched, developed, and released, and now neural networks are one of the elements of deep learning. Although we simply say "neural networks," there are various types and we'll look at the representative methods in order now.

Perceptrons (single-layer neural networks)

The perceptron algorithm is the model that has the simplest structure in the algorithms of neural networks and it can perform linear...