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Java: Data Science Made Easy

You're reading from   Java: Data Science Made Easy Data collection, processing, analysis, and more

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Product type Course
Published in Jul 2017
Publisher Packt
ISBN-13 9781788475655
Length 734 pages
Edition 1st Edition
Languages
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Authors (3):
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Alexey Grigorev Alexey Grigorev
Author Profile Icon Alexey Grigorev
Alexey Grigorev
Richard M. Reese Richard M. Reese
Author Profile Icon Richard M. Reese
Richard M. Reese
Jennifer L. Reese Jennifer L. Reese
Author Profile Icon Jennifer L. Reese
Jennifer L. Reese
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Toc

Table of Contents (29) Chapters Close

Title Page
Credits
Preface
1. Module 1 FREE CHAPTER
2. Getting Started with Data Science 3. Data Acquisition 4. Data Cleaning 5. Data Visualization 6. Statistical Data Analysis Techniques 7. Machine Learning 8. Neural Networks 9. Deep Learning 10. Text Analysis 11. Visual and Audio Analysis 12. Visual and Audio Analysis 13. Mathematical and Parallel Techniques for Data Analysis 14. Bringing It All Together 15. Module 2
16. Data Science Using Java 17. Data Processing Toolbox 18. Exploratory Data Analysis 19. Supervised Learning - Classification and Regression 20. Unsupervised Learning - Clustering and Dimensionality Reduction 21. Working with Text - Natural Language Processing and Information Retrieval 22. Extreme Gradient Boosting 23. Deep Learning with DeepLearning4J 24. Scaling Data Science 25. Deploying Data Science Models 26. Bibliography

Various mathematical libraries


There are numerous mathematical libraries available for Java use. In this section, we will provide a quick and high-level overview of several libraries. These libraries do not necessarily automatically support multiple processors. In addition, the intent of this section is to provide some insight into how these libraries can be used. In most cases, they are relatively easy to use.

A list of Java mathematical libraries is found at https://en.wikipedia.org/wiki/List_of_numerical_libraries#Java and https://java-matrix.org/. We will demonstrate the use of the jblas, Apache Commons Math, and the ND4J libraries.

Using the jblas API

The jblas API (http://jblas.org/) is a math library supporting Java. It is based on Basic Linear Algebra Subprograms (BLAS) (http://www.netlib.org/blas/) and Linear Algebra Package (LAPACK) (http://www.netlib.org/lapack/), which are standard libraries for fast arithmetic calculation. The jblas API provides a wrapper around these libraries...

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