We often hear that research for deep learning has always been ongoing and that's a fact. Many corporations, especially large tech companies such as Google, Facebook, Microsoft, and IBM, invest huge amounts of money into the research of deep learning, and we frequently hear news that some corporation has bought these research groups. However, as we look through, deep learning itself has various types of algorithms, and fields where these algorithms can be applied. Even so, it is a fact that is it not widely known which fields deep learning is utilized in or can be used in. Since the word "AI" is so broadly used, people can't properly understand which technology is used for which product. Hence, in this section, we will go through the fields where people have been trying to adopt deep learning actively for practical applications.
Java Deep Learning Essentials
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Java Deep Learning Essentials
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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!
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Java Deep Learning Essentials
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Table of Contents (15 chapters)
Java Deep Learning Essentials
Credits
About the Author
About the Reviewers
www.PacktPub.com
Preface
Free Chapter
Deep Learning Overview
Algorithms for Machine Learning – Preparing for Deep Learning
Deep Belief Nets and Stacked Denoising Autoencoders
Dropout and Convolutional Neural Networks
Exploring Java Deep Learning Libraries – DL4J, ND4J, and More
Approaches to Practical Applications – Recurrent Neural Networks and More
Other Important Deep Learning Libraries
What's Next?
Index
Customer Reviews