Book Image

Java: Data Science Made Easy

By : Richard M. Reese, Jennifer L. Reese, Alexey Grigorev
Book Image

Java: Data Science Made Easy

By: Richard M. Reese, Jennifer L. Reese, Alexey Grigorev

Overview of this book

Data science is concerned with extracting knowledge and insights from a wide variety of data sources to analyse patterns or predict future behaviour. It draws from a wide array of disciplines including statistics, computer science, mathematics, machine learning, and data mining. In this course, we cover the basic as well as advanced data science concepts and how they are implemented using the popular Java tools and libraries.The course starts with an introduction of data science, followed by the basic data science tasks of data collection, data cleaning, data analysis, and data visualization. This is followed by a discussion of statistical techniques and more advanced topics including machine learning, neural networks, and deep learning. You will examine the major categories of data analysis including text, visual, and audio data, followed by a discussion of resources that support parallel implementation. Throughout this course, the chapters will illustrate a challenging data science problem, and then go on to present a comprehensive, Java-based solution to tackle that problem. You will cover a wide range of topics – from classification and regression, to dimensionality reduction and clustering, deep learning and working with Big Data. Finally, you will see the different ways to deploy the model and evaluate it in production settings. By the end of this course, you will be up and running with various facets of data science using Java, in no time at all. This course contains premium content from two of our recently published popular titles: - Java for Data Science - Mastering Java for Data Science
Table of Contents (29 chapters)
Title Page
Credits
Preface
Free Chapter
1
Module 1
15
Module 2
26
Bibliography

Classifying text


Classifying text is an important part of machine learning and data science. We have to be able to classify text for a variety of applications, including document retrieval and web searches. It is often important to assign specific labels to the data before we can determine its usefulness for a particular application or search result.

In this chapter, we are going to demonstrate a technique involving the use of paragraph vectors and labeled data with DL4J classes. This example allows us to read in documents and, based on the text inside of the document, assign a label (or classification) to the document. We are also going to show an example of classifying text by similarity. This means we will match phrases and words that have similar structure. This example will also use DL4J.

Word2Vec and Doc2Vec

We will be using Word2Vec and Doc2Vec in a few examples in this chapter. Word2Vec is a neural network with two layers used for text processing. Given a body of text, the network will...