Power Java

Power Java

Mark Watson
Buy on Leanpub

Table of Contents

Power Java

  • 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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