Search icon CANCEL
Arrow left icon
Explore Products
Best Sellers
New Releases
Books
Videos
Audiobooks
Learning Hub
Conferences
Free Learning
Arrow right icon
Arrow up icon
GO TO TOP
Java: Data Science Made Easy

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

Arrow left icon
Product type Course
Published in Jul 2017
Publisher Packt
ISBN-13 9781788475655
Length 734 pages
Edition 1st Edition
Languages
Tools
Arrow right icon
Authors (3):
Arrow left icon
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
Arrow right icon
View More author details
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

Classifying text


Classifying text is an important part of machine learning and data science. We have to be able to classify text for a variety of applications, including document retrieval and web searches. It is often important to assign specific labels to the data before we can determine its usefulness for a particular application or search result.

In this chapter, we are going to demonstrate a technique involving the use of paragraph vectors and labeled data with DL4J classes. This example allows us to read in documents and, based on the text inside of the document, assign a label (or classification) to the document. We are also going to show an example of classifying text by similarity. This means we will match phrases and words that have similar structure. This example will also use DL4J.

Word2Vec and Doc2Vec

We will be using Word2Vec and Doc2Vec in a few examples in this chapter. Word2Vec is a neural network with two layers used for text processing. Given a body of text, the network will...

lock icon The rest of the chapter is locked
Register for a free Packt account to unlock a world of extra content!
A free Packt account unlocks extra newsletters, articles, discounted offers, and much more. Start advancing your knowledge today.
Unlock this book and the full library FREE for 7 days
Get unlimited access to 7000+ expert-authored eBooks and videos courses covering every tech area you can think of
Renews at €18.99/month. Cancel anytime