Book Image

Mastering Java for Data Science

By : Alexey Grigorev
Book Image

Mastering Java for Data Science

By: Alexey Grigorev

Overview of this book

Java is the most popular programming language, according to the TIOBE index, and it is a typical choice for running production systems in many companies, both in the startup world and among large enterprises. Not surprisingly, it is also a common choice for creating data science applications: it is fast and has a great set of data processing tools, both built-in and external. What is more, choosing Java for data science allows you to easily integrate solutions with existing software, and bring data science into production with less effort. This book will teach you how to create data science applications with Java. First, we will revise the most important things when starting a data science application, and then brush up the basics of Java and machine learning before diving into more advanced topics. We start by going over the existing libraries for data processing and libraries with machine learning algorithms. After that, we cover topics such as classification and regression, dimensionality reduction and clustering, information retrieval and natural language processing, and deep learning and big data. Finally, we finish the book by talking about the ways to deploy the model and evaluate it in production settings.
Table of Contents (17 chapters)
Title Page
About the Author
About the Reviewers
Customer Feedback

Neural Networks and DeepLearning4J

Neural Networks are typically good models that give a reasonable performance on structured datasets, but they might not necessarily be better than others. However, when it comes to dealing with unstructured data, most often they are the best.

In this chapter, we will look into a Java library for designing Deep Neural Networks, called DeepLearning4j. But before we do this, we first will look into its backend--ND4J, which does all the number crunching and heavy lifting.

ND4J - N-dimensional arrays for Java

DeepLearning4j relies on ND4J for preforming linear algebra operations such as matrix multiplication. Previously, we covered quite a few such libraries, for example, Apache Commons Math or Matrix Toolkit Java. Why do we need yet another linear algebra library?

There are two reasons for this. First, these libraries usually deal only with vectors and matrices, but for deep learning we need tensors. A tensor is a generalization of vectors and matrices to multiple...