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 about two deep learning algorithms that don't require pre-training: deep neural networks with dropout and CNN. The key to high precision rates is how we make the network sparse, and dropout is one technique to achieve this. Another technique is the rectifier, the activation function that can solve the problem of saturation that occurred in the sigmoid function and the hyperbolic tangent. CNN is the most popular algorithm for image recognition and has two features: convolution and max-pooling. Both of these attribute the model to acquire translation invariance. If you are interested in how dropout, rectifier, and other activation functions contribute to the performance of neural networks, the following could be good references: Deep Sparse Rectifier Neural Networks (Glorot, et. al. 2011,, ImageNet Classification with Deep Convolutional Neural Networks (Krizhevsky et. al. 2012, https:/...