Practical Artificial Intelligence Programming With Java
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Practical Artificial Intelligence Programming With Java

Fifth Edition (July 2020)

About the Book

This book covers a variety of topics dealing with Artificial Intelligence, Neural Networks and Deep Learning, Information Gathering, and two new chapters on the Semantic Web (and one previous chapter on the Semantic Web that has been heavily modified)

The book example code is dual licenced under the Apache 2 and GPL 3 licenses. You may want to look at the book example programs at the github repository for this book before purchasing this book.

All of my Leanpub books, including this book, can be read for FREE on my web site:

About the Author

Mark Watson
Mark Watson

Interested in the author? If you are then visit Mark's web site and read his blog.

Mark Watson is a consultant who works out of his home office in Sedona Arizona. His consulting customer list includes: Google, CompassLabs, Disney, Arctan Group,,, and Webmind Corporation.

Mark Watson

Episode 253

Table of Contents

  • Preface
    • Personal Artificial Intelligence Journey
    • Maven Setup for Combining Examples in this Book
    • Software Licenses for Example Programs in this Book
    • Acknowledgements
  • Search
    • Representation of Search State Space and Search Operators
    • Finding Paths in Mazes
    • Finding Paths in Graphs
    • Adding Heuristics to Breadth-first Search
    • Heuristic Search and Game Playing: Tic-Tac-Toe and Chess
  • Reasoning
    • Logic
    • PowerLoom Overview
    • Running PowerLoom Interactively
    • Using the PowerLoom APIs in Java Programs
    • Suggestions for Further Study
  • 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
  • Genetic Algorithms
    • Theory
    • Java Library for Genetic Algorithms
    • Finding the Maximum Value of a Function
  • Neural Networks
    • Road Map for the Neural Network Example Code
    • Backpropagation Neural Networks
    • A Java Class Library for Back Propagation
    • Adding Momentum to Speed Up Back-Prop Training
    • Wrap-up for Neural Networks
  • Deep Learning Using Deeplearning4j
    • Feed Forward Classification Networks
    • Feed Forward Example
    • Configuring the Example Using Maven
    • Documentation for Other Types of Deep Learning Layers
    • Running the DL4J Example Programs and Modifying Them For Your Use
    • Modifying the Character Generating LSTM Example to Model and Generate CSV Spreadsheet Data
    • Roadmap for the DL4J Model Zoo
    • Deep Learning Wrapup
  • Natural Language Processing
    • Overview of the NLP Library and Running the Examples
    • Tokenizing, Stemming, and Part of Speech Tagging Text
    • Named Entity Extraction From Text
    • Automatically Assigning Categories to Text
    • Text Clustering
    • Wrapup
  • 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
  • Combining the WordNet Linguistic Database With OpenNLP
    • Using the WordNet Linguistic Database
    • Installing the Libraries and Linguistic Data for this Example
    • Implementation
    • Other Type Relationships Supported by WordNet
    • Wrap-up and Ideas for Using WordNet
  • Information Gathering
    • Web Scraping Examples
    • DBPedia Entity Lookup
    • Client for GeoNames Service
    • Wrap-up for Information Gathering
  • Resolve Entity Names to DBPedia References
    • DBPedia Entities
    • Wrap-up for Resolving Entity Names to DBPedia References
  • Semantic Web
    • Available Tools
    • Relational Database Model Has Problems Dealing with Rapidly Changing Data Requirements
    • RDF: The Universal Data Format
    • Extending RDF with RDF Schema
    • The SPARQL Query Language
    • Using Jena
    • OWL: The Web Ontology Language
    • Semantic Web Wrap-up
  • Automatically Generating Data for Knowledge Graphs
    • Implementation Notes
    • Generating RDF Data
    • KGCreator Wrap Up
  • Knowledge Graph Navigator
    • Entity Types Handled by KGN
    • General Design of KGN with Example Output
    • UML Class Diagram for Example Application
    • Implementation
    • Wrap-up
  • Conclusions

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