Introduction to Cognitive Computing
Introduction to Cognitive Computing


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Introduction to Cognitive Computing

This book is 100% complete

Completed on 2018-11-03

About the Book

We will start by reviewing Philosophy (study of Ontology's and Knowledge Representation), an overview of general Artificial Intelligence, Linguistics (understanding language will help us better extract useful information from English language text), and Neuroscience (to better understand how our minds work).

The home page for the resources for this book are both the author's web site and the github repository

There are three parts to this book:

Part I - A Dive into Human Cognition and Cognitive Science

This section of the book will ground you in the science that forms the foundation of Cognition Technology with chapters on Philosophy (especially how it pertains to Knowledge Management and Knowledge Representation), Linguistics, general AI, and Neuroscience.

Part II - Using Machine Learning and Deep Learning Neural Networks to Model Cognition

We use Deep Learning Neural Networks for classification, logistic regression, Knowledge Representation, and Natural Language Processing. We start with some simple standalone programs (written in TypeScript, with JavaScipt versions also included) and then use Google's Tensorflow machine learning library for more complex examples. Tensorflow runs well for moderate size problems on your laptop and scales up using Google's Cloud Platform (or your own servers with GPU support). Currently deep learning networks are the most interesting and useful technology for modeling cognition. The author's primary personal interests in deep learning are NLP and language models.

Part III - Natural Language Processing and Knowledge Representation

Here we will dive deeper into practical applications of Natural Language Processing.

About the Author

Mark Watson
Mark Watson

Mark Watson a consultant specializing in deep learning, machine learning, and general artificial intelligence software development. He uses Python, Common Lisp, Java, Clojure, Haskell, and Ruby for development.

He is the author of 23 published books on Artificial Intelligence, Deep Learning, Java, Ruby, Machine Learning, Common LISP, Clojure, JavaScript, Semantic Web, NLP, C++, Linux, and Scheme.

Mark's consulting customer list includes: Google, Capital One, CompassLabs, Disney,,, and Webmind Corporation.

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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