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


In this chapter, you learned how to utilize deep learning algorithms for practical applications. The fields that are well studied are image recognition and NLP. While learning about the field of NLP, we looked through two new deep learning models: the RNN and LSTM networks, which can be trained with time sequential data. The training algorithm used in these models is BPTT. You also learned that there are three approaches to make the best of the deep learning ability: the field-oriented approach, the breakdown-oriented approach, and the output-oriented approach. Each approach has a different angle, and can maximize the possibility for deep learning.

And …congratulations! You've just accomplished the learning part of deep learning with Java. Although there are still some models that have not been mentioned yet in this book, you can be sure there will be no problem in acquiring and utilizing them. The next chapter will introduce some libraries that are implemented with other programming...