Artificial Intelligence Programming in Python: Exploring the Boundaries of Deep Learning, Symbolic AI, and Knowledge Representation

Artificial Intelligence Programming in Python: Exploring the Boundaries of Deep Learning, Symbolic AI, and Knowledge Representation

Hybrid Artificial Intelligence

About the Book

The tutorial material starts with a review AI topics with background information. We will then proceed to material on Python development, getting you setup for the later cookbook style examples.

We will cover Deep Learning, old fashioned symbolic AI, broad coverage of data sources as well as wide range of tups 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.

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:

Table of Contents

  • Cover Material, Copyright, and License
  • Preface to Second Edition
    • Author Biography and Experience
    • Programming Examples in this Book
    • Why Use the Cloud?
    • A Request from the Author
    • Credits
  • Part I - A Dive into Human Cognition and Cognitive Science
  • An Introduction to Cognitive Computing
    • Overview
    • An Overview of How the Human Brain Works
    • Introduction to Artificial Neural Networks
    • Symbolic Representation of Facts and Rules (Expert Systems)
    • Symbolic Models for Natural Language Processing
    • The Public’s Perception of Cognitive Computing and Artificial Intelligence
  • An Overview of Linguistics
    • Phonology
    • Morphology
    • Syntax
    • Semantics
    • Pragmatics
    • Linguistics Wrap Up And Practical Applications
  • AI and Society
    • AI and the Future of Jobs
    • Ethical AI
  • Part II - Practical Applications of Cognitive Computing
  • (Almost) Everything You Really Need To Know About Machine Learning
    • Introduction to Supervised and Unsupervised Learning
    • Linear Regression
    • A More Complex models using Google’s Neural Network Playground
    • Backpropagation Neural Networks
    • A More Complex Neural Network Example
    • Feature Engineering
  • Using TensorFlow for Implementing Deep Neural Networks
    • Installing TensorFlow and Keras
    • Processing Cancer Data With a BackPropagation Network
    • Deep Learning Wrap Up
  • Tools and Techniques for Natural Language Processing
    • Installation and Introduction to the spaCy NLP Library
    • Using spaCY for Assigning Part of Speech Tags
    • Using spaCY for Entity Recognition
    • Introduction to the OpenNLP Library and Installation Notes
    • Classification Example Using the OpenNLP Library
    • Training a New Categorization Model using Facebook’s fasttext
    • NLP Wrap Up
  • Book Wrap Up
    • My Other Books That Might Interest You
  • Appendix A - Installing TensorFlow
    • Python 2.7
    • Python 3.5
    • Dealing With New Versions of TensorFlow
  • Appendix B - Installing OpenNLP
  • Appendix C - Installing spaCy
  • Appendix D - Using Cloud Computing Platforms

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