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Mastering Java for Data Science

You're reading from Mastering Java for Data Science Analytics and more for production-ready applications

Product type Paperback

Published in Apr 2017

Publisher Packt

ISBN-13 9781782174271

Length 364 pages

Edition 1st Edition

Languages

Java

Tools

Hadoop

Concepts

Data Science

Author (1):

Alexey Grigorev

View More author details

Table of Contents (11) Chapters

Preface

1. Data Science Using Java

2. Data Processing Toolbox FREE CHAPTER

3. Exploratory Data Analysis

4. Supervised Learning - Classification and Regression

5. Unsupervised Learning - Clustering and Dimensionality Reduction

6. Working with Text - Natural Language Processing and Information Retrieval

7. Extreme Gradient Boosting

8. Deep Learning with DeepLearning4J

9. Scaling Data Science

10. Deploying Data Science Models

Summary

In this chapter, we looked at how we can use deep learning in Java applications, learned the basics of the DeepLearning4j library, and then tried to apply it to an image recognition problem where we wanted to classify images to dogs and cats.

In the next chapter, we will cover Apache Spark--a library for distributing data science algorithms across a cluster of machines.

The rest of the chapter is locked

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Authors (1)

Alexey Grigorev

Alexey Grigorev

Alexey Grigorev is a skilled data scientist, machine learning engineer, and software developer with more than 8 years of professional experience. He started his career as a Java developer working at a number of large and small companies, but after a while he switched to data science. Right now, Alexey works as a data scientist at Simplaex, where, in his day-to-day job, he actively uses Java and Python for data cleaning, data analysis, and modeling. His areas of expertise are machine learning and text mining.

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