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

Convolutional neural networks

All the machine learning/deep learning algorithms you have learned about imply that the type of input data is one-dimensional. When you look at a real-world application, however, data is not necessarily one-dimensional. A typical case is an image. Though we can still convert two-dimensional (or higher-dimensional) data into a one-dimensional array from the standpoint of implementation, it would be better to build a model that can handle two-dimensional data as it is. Otherwise, some information embedded in the data, such as positional relationships, might be lost when flattened to one dimension.

To solve this problem, an algorithm called Convolutional Neural Networks (CNN) was proposed. In CNN, features are extracted from two-dimensional input data through convolutional layers and pooling layers (this will be explained later), and then these features are put into general multi-layer perceptrons. This preprocessing for MLP is inspired by human visual areas and...