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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
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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
2. Getting Started with Data Science FREE CHAPTER 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

Chapter 23. Deep Learning with DeepLearning4J

In the previous chapter, we covered Extreme Gradient Boosting (XGBoost)--a library that implements the gradient boosting machine algorithm. This library provides state-of-the-art performance for many supervised machine learning problems. However, XGBoost only shines when the data is already structured and there are good handmade features.

The feature engineering process is usually quite complex and requires a lot of effort, especially when it comes to unstructured information such as images, sounds, or videos. This is the area where deep learning algorithms are usually superior to others, including XGBoost; they do not need hand-crafted features and are able to learn the structure of the data themselves.

In this chapter, we will look into a deep learning library for Java--DeepLearning4J. This library allows us to easily specify complex neural network architectures that are able to process unstructured data such as images. In particular, we will...

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