Practical Artificial Intelligence Programming With Clojure
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Practical Artificial Intelligence Programming With Clojure

About the Book

A practical guide to Artificial Intelligence development using Clojure that uses OpenAI APIs, DeepLearning4J, NLP, anomaly detection, and knowledge graph tools in Clojure applications.

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,,, and Webmind Corporation.

All of my Leanpub books are available to read for free on my web site:

Mark Watson

Episode 253

Table of Contents

  • Cover Material, Copyright, and License
  • Preface
    • Clojure, With Some Java and Python
    • Personal Artificial Intelligence Journey: or, Life as a Lisp Developer
    • Acknowledgements
  • Deep Learning Using Deeplearning4j
    • Feed Forward Classification Networks
    • Optional Material: Documentation For Other Types of DeepLearning4J Built In Layers
    • Deep Learning Wrap Up
  • Natural Language Processing Using OpenNLP
    • Using the Clojure and Java Wrappers for OpenNLP
  • Anomaly Detection Machine Learning Example
    • Motivation for Anomaly Detection
    • Math Primer for Anomaly Detection
    • AnomalyDetection Utility Class Written in Java
    • Clojure Experiment for the University of Wisconsin Cancer Data Using Java Anomaly Detection Code
  • Web Scraping
    • Web Scraping Using the jsoup Library
  • Background Material for the Semantic Web and Knowledge Graphs
    • Learning Plan
    • Available Tools
    • RDF: The Universal Data Format
    • Extending RDF with RDF Schema
    • The SPARQL Query Language
    • OWL: The Web Ontology Language
    • Semantic Web Wrap-up
  • Clojure Wrapper for the Jena RDF and SPARQL Library
  • Knowledge Graph Navigator
    • Entity Types Handled by KGN
    • KGN Implementation
  • Using the OpenAI APIs
  • Question Answering Using OpenAI APIs and a Local Embeddings Vector Database
    • Implementing a Local Vector Database for Document Embeddings
    • Create Local Embeddings Vectors From Local Text Files With OpenAI GPT APIs
    • Using Local Embeddings Vector Database With OpenAI GPT APIs
    • Wrap Up for Using Local Embeddings Vector Database to Enhance the Use of GPT3 APIs With Local Documents
  • Conclusions

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