This repository provides Java code examples for the book "Practical Artificial Intelligence With Java," focusing on Large Language Models, deep learning, and knowledge graphs, along with updated examples of genetic algorithms, anomaly detection, and web scraping. It's intended for developers and students looking to implement AI concepts in Java.

How It Works

The project offers a collection of Java code snippets and examples directly supporting the chapters of the associated book. It covers a range of AI topics, from foundational algorithms like genetic algorithms to modern applications such as LLMs and deep learning, demonstrating practical implementation within the Java ecosystem.

Quick Start & Requirements

• Primary install / run command: Clone the repository and import the projects into an IDE like IntelliJ IDEA or Eclipse. Specific execution instructions will be in the book.
• Prerequisites: Java Development Kit (JDK) 17 or later is recommended. Some examples may require additional libraries or external services (e.g., API keys for LLMs), detailed in the book.
• Setup: Assumed to be straightforward for users familiar with Java development environments.
• Links: Leanpub Link for latest edition: https://leanpub.com/java-ai-book

Highlighted Details

• Covers a broad spectrum of AI, from traditional algorithms to cutting-edge LLMs.
• Includes practical examples for knowledge graph creation and utilization.
• Integrates concepts from previous, related books by the author.

Maintenance & Community

• The project is tied to the author's book, with the latest edition updated in April 2024. Further community interaction details are not specified in the README.

Licensing & Compatibility

• The repository itself does not explicitly state a license. The code is provided for use with the author's book, implying a permissive use for educational purposes related to the book. Compatibility for broader commercial use is not specified.

Limitations & Caveats

The code examples are directly tied to the book's content and may not represent standalone, production-ready applications. Some advanced examples, particularly those involving LLMs, might require external API access or significant computational resources not detailed here.