Practical Artificial Intelligence Programming With Java
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: https://markwatson.com/
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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