Practical Artificial Intelligence Programming With Clojure
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
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
-
Using the Brave Search APIs
- Setting an Environment Variable for the Access Key for Brave Search APIs
- Using the Brave Search API
- Wrap-up
-
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
-
Simple RDF Datastore and Partial SPARQL Query Processor
- Implementing a Simple RDF Datastore
- Querying the RDF Datastore
- Implementing a Partial SPARQL Query Processor
- Putting It All Together: Running Example Queries
- Summary
-
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
-
Running LLMs Locally Using Ollama
- Running the Example Tests
- Clojure Client Library for the Ollama Service REST APIs
- Conclusions
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