Practical Artificial Intelligence Programming With Java
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Completed on 2017-11-24
About the Book
This book covers a variety of topics dealing with Artificial Intelligence, Information Gathering, Data Science, and the Semantic Web.
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.
About the Author
Table of Contents
- Preface
-
Introduction
- Other JVM Languages
- Github Repository for Book Software
- Use of Java Generics and Native Types
- Notes on Java Coding Styles Used in this Book
- Book Summary
-
Search
- Representation of Search State Space and Search Operators
- Finding Paths in Mazes
- Finding Paths in Graphs
- Adding Heuristics to Breadth First Search
- 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
-
Semantic Web
- 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 Sesame
- OWL: The Web Ontology Language
- Knowledge Representation and REST
- Material for Further Study
-
Expert Systems
- Production Systems
- The Drools Rules Language
- Using Drools in Java Applications
- Example Drools Expert System: Blocks World
- Example Drools Expert System: Help Desk System
- Notes on the Craft of Building Expert Systems
-
Genetic Algorithms
- Theory
- Java Library for Genetic Algorithms
- Finding the Maximum Value of a Function
-
Machine Learning with Weka
- Using Weka’s Interactive GUI Application
- Interactive Command Line Use of Weka
- Embedding Weka in a Java Application
- Suggestions for Further Study
-
Neural Networks
- Hopfield Neural Networks
- Java Classes for Hopfield Neural Networks
- Testing the Hopfield Neural Network Class
- Back Propagation Neural Networks
- A Java Class Library for Back Propagation
- Adding Momentum to Speed Up Back-Prop Training
-
Statistical Natural Language Processing
- Tokenizing, Stemming, and Part of Speech Tagging Text
- Named Entity Extraction From Text
- Using the WordNet Linguistic Database
- Automatically Assigning Tags to Text
- Text Clustering
- Spelling Correction
- Hidden Markov Models
- Wrapup
-
Information Gathering
- Open Calais
- Information Discovery in Relational Databases
- Down to the Bare Metal: In-Memory Index and Search
- Indexing and Search Using Embedded Lucene
- Indexing and Search with Nutch Clients
-
Data Science Techniques
- A Mix of Open Source and Proprietary Tools
- Handling “small big data” in a Cost Effective Way
- Writing and Testing MapReduce Applications
- Example Application: MapReduce Application for Finding Proper Names in Text
- Using Inexpensive Large Memory Leased Servers
- Example Application Idea: Using the Google Book Project NGRAM Data Sets
- Example Application Idea: Using Wikipedia Data Dumps
- Conclusion
- Conclusions
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