Practical Python Artificial Intelligence Programming

Description

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