Practical Python Artificial Intelligence Programming

✍ Scribed by Mark Watson

• Tongue: English
• Category: Library

✦ Synopsis

This book is intended, dear reader, to show you a wide variety of practical AI techniques and
examples, and to be a jumping off point when you discover things that interest you or may be useful
in your work. A common theme here is covering AI programming tasks that used to be difficult or
impossible but are now much simpler using deep learning, of at least possible. I also cover a wide
variety on non-deep learning material including a chapter on Symbolic AI that has historic interest
and some current practical value.

✦ Table of Contents

Cover Material, Copyright, and License
Preface
About the Author
Using the Example Code
Book Cover
Acknowledgements
Part I - Getting Started
Python Development Environment
Managing Python Versions and Libraries
Editors and IDEs
Code Style
“Classic” Machine Learning
Example Material
Classification Models using Scikit-learn
Classic Machine Learning Wrap-up
Symbolic AI
Comparison of Symbolic AI and Deep Learning
Implementing Frame Data Structures in Python
Use Predicate Logic by Calling Swi-Prolog
Swi-Prolog and Python Deep Learning Interop
Soar Cognitive Architecture
Constraint Programming with MiniZinc and Python
Good Old Fashioned Symbolic AI Wrap-up
Part II - Knowledge Representation
Getting Setup To Use Graph and Relational Databases
The Apache Jena Fuseki RDF Datastore and SPARQL Query Server
The Neo4j Community Edition and Cypher Query Server and the Memgraph Graph Database
The SQLite Relational Database
Semantic Web, Linked Data and Knowledge Graphs
Overview and Theory
A Hybrid Deep Learning and RDF/SPARQL Application for Question Answering
Knowledge Graph Creator: Convert Text Files to RDF Data Input Data for Fuseki
Old Technology: The OpenCyc Knowledge Base (Optional Material)
Examples Using Wikidata Instead of DBPedia
Knowledge Graph Navigator: Use English to Explore DBPedia
Wrap Up for Semantic Web, Linked Data and Knowledge Graphs
Part III - Deep Learning
The Basics of Deep Learning
Using TensorFlow and Keras for Building a Cancer Prediction Model
Natural Language Processing Using Deep Learning
OpenAI GPT-3 APIs
Hugging Face APIs
Comparing Sentences for Similarity Using Transformer Models
Deep Learning Natural Language Processing Wrap-up
Part IV - Overviews of Image Generation, Reinforcement Learning, and Recommendation Systems
Overview of Image Generation
Recommended Reading for Image Generation
Overview of Reinforcement Learning (Optional Material)
Overview
Available RL Tools
Reinforcement Learning Wrap-up
Overview of Recommendation Systems
TensorFlow Recommenders
Recommendation Systems Wrap-up
Book Wrap-up