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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
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Authors (3):
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Alexey Grigorev Alexey Grigorev
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Alexey Grigorev
Richard M. Reese Richard M. Reese
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Richard M. Reese
Jennifer L. Reese Jennifer L. Reese
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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

Training a neural network


There are three basic training approaches:

  • Supervised learning - With supervised learning the model is trained with data that matches input sets to output values
  • Unsupervised learning - In unsupervised learning, the data does not contain results, but the model is expected to determine relationships on its own
  • Reinforcement learning - Similar to supervised learning, but a reward is provided for good results

These datasets differ in the information they contain. Supervised and reinforcement learning contain correct output for a set of input. The unsupervised learning does not contain correct results.

A neural network learns (at least with supervised learning) by feeding an input into a network and comparing the results, using the activation function, to the expected outcome. If they match, then the network has been trained correctly. If they don't match then the network is modified.

When we modify the weights we need to be careful not to change them too drastically. If...

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