Mark Watson · leanpub.com

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About

This book covers a variety of topics dealing with Artificial Intelligence, Large Language Models, 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.

Author

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, Sitescout.com, Embed.ly, and Webmind Corporation.

The Leanpub Podcast

Podcast

Contents

Preface

  1. Requests from the Author
  2. Personal Artificial Intelligence Journey
  3. Maven Setup for Combining Examples in this Book
  4. Acknowledgements

Search

  1. Representation of Search State Space and Search Operators
  2. Finding Paths in Mazes
  3. Finding Paths in Graphs
  4. Adding Heuristics to Breadth-first Search
  5. Heuristic Search and Game Playing: Tic-Tac-Toe and Chess
  6. Optional Practice Problems

Using the OpenAI Large Language Model APIs in Java

  1. Java Library to Use OpenAI’s APIs
  2. Example Applications
  3. Extraction of Facts and Relationships from Text Data
  4. Using LLMs to Summarize Text
  5. Optional Practice Problems

Using the Google Gemini Large Language Model APIs in Java

  1. Java Library to Use Google Gemini’s APIs
  2. Example Applications
  3. Wrap Up
  4. Optional Practice Problems

Using Local LLMs with Ollama in Java Applications

  1. Advantages of Using Local LLMs with Ollama
  2. Applications of Local LLMs with Ollama
  3. Java Library to Use Ollama’s REST API
  4. Example Using the Library
  5. Extraction of Facts and Relationships from Text Data
  6. Using LLMs to Summarize Text
  7. Optional Practice Problems

LangChain for Java as an Abstraction for Different Large Language Models

  1. Why Use an Abstraction Layer?
  2. Maven Project Setup
  3. Implementation: The OllamaLlmLangChain4j Class
  4. Prompt Templates: Two-Shot Entity Extraction
  5. Test Examples
  6. Running the Examples
  7. Wrap Up
  8. Optional Practice Problems

AgentScope Agent Oriented Framework.

  1. Using the Java Implementation of AgentScope: Hello World
  2. Using the Java Implementation of AgentScope: Tool Use
  3. Wrap Up: From Simple Chat to Autonomous Tool Use
  4. Optional Practice Problems

Anomaly Detection Machine Learning Example

  1. Motivation for Anomaly Detection
  2. Math Primer for Anomaly Detection
  3. AnomalyDetection Utility Class
  4. Example Using the University of Wisconsin Cancer Data
  5. Optional Practice Problems

Genetic Algorithms

  1. Theory
  2. Java Library for Genetic Algorithms
  3. Finding the Maximum Value of a Function
  4. Optional Practice Problems

Neural Networks

  1. Road Map for the Neural Network Example Code
  2. Backpropagation Neural Networks
  3. A Java Class Library for Back Propagation
  4. Adding Momentum to Speed Up Back-Prop Training
  5. Wrap-up for Neural Networks
  6. Optional Practice Problems

Natural Language Processing

  1. Overview of the NLP Library and Running the Examples
  2. Tokenizing, Stemming, and Part of Speech Tagging Text
  3. Named Entity Extraction From Text
  4. Automatically Assigning Categories to Text
  5. Text Clustering
  6. Wrapup
  7. Optional Practice Problems

Information Gathering

  1. Web Scraping Examples
  2. Web Spidering Using the Jericho Library
  3. Client for GeoNames Service
  4. Wrap-up for Information Gathering
  5. Optional Practice Problems

Resolve Entity Names to DBPedia References

  1. DBPedia Entities
  2. Library Implementation

Also by the Author

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