Practical Artificial Intelligence Development With Racket
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Practical Artificial Intelligence Development With Racket

Using Racket Scheme for implementing many short AI examples including LLMs, vector datastore, NLP, semantic web and non-AI utilities.

About the Book

Using Racket Scheme for implementing many short AI examples including LLMs, vector datastore, NLP, semantic web and non-AI utilities.

This is a live eBook and is frequently updated. There will never be a "second edition" of this live eBook, rather, the free to read web version and PDFs and eBooks for purchasers get updated as new material is added and corrections are made.

About the Author

Mark Watson
Mark Watson

Mark Watson is a consultant specializing in deep learning, machine learning, knowledge graphs, and general artificial intelligence software development. He uses Common Lisp, Clojure, Python, Java, Haskell, and Ruby for development.

He is the author of 20+ published books on Artificial Intelligence, Deep Learning, Java, Ruby, Machine Learning, Common LISP, Clojure, JavaScript, Semantic Web, NLP, C++, Linux, and Scheme. He has 55 US Patents.

Mark's consulting customer list includes: Google, Capital One, Olive AI, CompassLabs, Disney, Sitescout.com, Embed.ly, and Webmind Corporation.

All of my Leanpub books are available to read for free on my web site:  https://markwatson.com/

Table of Contents

  • Preface
    • License for Book Manuscript: Creative Commons
    • Book Example Programs
    • Racket, Scheme, and Common Lisp
    • Personal Artificial Intelligence Journey: or, Life as a Lisp Developer
    • Acknowledgements
  • A Quick Racket Tutorial
    • Installing Packages
    • Installing Local Packages In Place
    • Mapping Over Lists
    • Hash Tables
    • Racket Structure Types
    • Simple HTTP GET and POST Operations
    • Using Racket ~/.racketrc Initialization File
    • Tutorial Wrap Up
  • Datastores
    • Accessing Public RDF Knowledge Graphs - a DBPedia Example
    • Sqlite
  • Web Scraping
    • Getting Started Web Scraping
    • Implementation of a Racket Web Scraping Library
  • Using the OpenAI, Anthropic, Mistral, and Local Hugging Face Large Language Model APIs in Racket
    • Introduction to Large Language Models
    • Using the OpenAI APIs in Racket
    • Using the Anthropic APIs in Racket
    • Using a Local Hugging Face Llama2-13b-orca Model with Llama.cpp Server
    • Using a Local Mistral-7B Model with Ollama.ai
  • Retrieval Augmented Generation of Text Using Embeddings
    • Example Implementation
    • Retrieval Augmented Generation Wrap Up
  • Natural Language Processing
    • NLP Wrap Up
  • Knowledge Graph Navigator
    • Entity Types Handled by KGN
    • KGN Implementation
    • Knowledge Graph Navigator Wrap Up
  • Conclusions

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