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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

Case study - hardware performance


In this project, we will try to predict how much time it will take to multiply two matrices on different computers.

The dataset for this project originally comes from the paper Automatic selection of the fastest algorithm implementation by Sidnev and Gergel (2014), and it was made available at a machine learning competition organized by Mail.RU. You can check the details at http://mlbootcamp.ru/championship/7/.

Note

The content is in Russian, so if you do not speak it, it is better to use a browser with translation support.

You will find a copy of the dataset along with the code for this chapter.

This dataset has the following data:

  • m, k, and n represent the dimensionality of the matrices, with m*k being the dimensionality of matrix A and k*n being the dimensionality of matrix B
  • Hardware characteristics such as CPU speed, number of cores, whether hyper-threating is enabled or not, and the type of CPU
  • The operation system

The solution for this problem can be...

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