Practical Python Artificial Intelligence Programming
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Practical Python Artificial Intelligence Programming

About the Book

We will cover Deep Learning, old fashioned symbolic AI, broad coverage of data sources as well as wide range of tips and techniques for using Python both in experiments and in production.

The author has been a general AI practitioner since 1982, developed neural network products and projects since 1986, and deep learning since 2015. He has written 20+ books and has 50+ US Patents.

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
      • 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 and ChatGPT 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
      • An Introduction to Markov Decision Process
      • A Concrete Example Implementing RL
      • Reinforcement Learning Wrap-up
    • Overview of Recommendation Systems
      • TensorFlow Recommenders
      • Recommendation Systems Wrap-up
    • Book Wrap-up

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