Power Java
Power Java
About the Book
This book is based on the author's experience as a developer and consultant and consists of seven chapters:
- Network programming techniques for the Internet of Things (IoT)
- Natural Language Processing using OpenNLP including using existing models and creating your own models
- Machine learning using the Spark mllib library (document custering, logistic regression, word2vec similarity)
- Anomaly detection machine learning example
- Web scraping and information gathering
- Using rich semantic and linked data sources on the web to enrich the data models you use in your applications
- Java Strategies for Knowledge Management using local and cloud data
The first chapter on IoT is a tutorial on network programming techniques for IoT development. I have also used these same techniques for multiplayer game development and distributed virtual reality systems. This chapter stands on its own and is not connected to any other material in this book. To be clear, this chapter covers some of the network programming techniques you will need for IoT applications and does not cover development using IoT devices.
The second chapter shows you how to use the OpenNLP library to use machine learning to train your own maximum entropy classifiers and to segment sentences, tag parts of speech, and generally process English language text. Both this chapter and the next chapter on machine learning using the Spark MLlib library use machine learning techniques. The Spark MLlib is convenient to use for development on your laptop and you can use the same code you develop on Spark clusters to get near real time processing of big data.
The last two chapters are for information architects or developers who would like to develop information design and knowledge management skills. I stress the idea of leveraging both cloud data (e.g., Microsoft Office 365 and Google Drive) and local data sources. In order to simplify the final example program in the book, I use Google Takeout to export my data (Microsoft Word and Excel file formats, mailbox, and iCal calendar files). It is left as a project for the reader to extend the example program to interface with the cloud data sources their organization uses.
License
About the Author
Table of Contents
-
Preface
- Book Outline
- If You Did Not Buy This Book
-
Network Programming Techniques for the Internet of Things
- Motivation for IoT
- Running the example programs
- Design Pattern
- Directory Lookups
- User Data Protocol Network Programming
- Multicast/Broadcast Network Programming
- Wrap Up on IoT
-
Natural Language Processing Using OpenNLP
- Using OpenNLP Pre-Trained Models
- Training a New Categorization Model for OpenNLP
- Using Our New Trained Classification Model
- Using the OpenNLP Parsing Model
-
Machine Learning Using Apache Spark
- Setting Up Spark On Your Laptop
- Hello Spark - a Word Count Example
- Introducing the Spark MLlib Machine Learning Library
- MLlib Logistic Regression Example Using University of Wisconsin Cancer Database
- MLlib SVM Classification Example Using University of Wisconsin Cancer Database
- MLlib K-Means Example Program
- Converting Text to Numeric Feature Vectors
- Using K-Means to Cluster Wikipedia Articles
- Using SVM for Text Classification
- Using word2vec To Find Similar Words In Documents
- Chapter Wrap Up
-
Anomaly Detection Machine Learning Example
- Motivation for Anomaly Detection
- Math Primer for Anomaly Detection
- AnomalyDetection Utility Class
- Example Using the University of Wisconsin Cancer Data
-
Deep Learning Using Deeplearning4j
- Deep Belief Networks
- Deep Belief Example
- Deep Learning Wrapup
-
Web Scraping Examples
- Motivation for Web Scraping
- Using the jsoup Library
- Wrap Up
-
Linked Data
- Example Code
- Overview of RDF and SPARQL
- SPARQL Query Client
- DBPedia Entity Lookup
- Annotate Text with DBPedia Entity URIs
- Resolving Named Entities in Text to Wikipedia URIs
- Combining Data from Public and Private Sources
- Wrap Up for Linked Data
-
Java Strategies for Working with Cloud Data: Knowledge Management-Lite
- Motivation for Knowledge Management
- Using Google Drive Cloud Takeout Service
- Using Postgres as a Local Document Store with Text Search
- Wrap Up
- Book Wrap Up
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