Artificial Intelligence Using Swift
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Artificial Intelligence Using Swift

CoreML, NLP, Deep Learning, Semantic Web and Linked Data, Knowledge Graphs, Knowledge Representation

About the Book

This book focuses on the author's professional interests: NLP, deep learning, knowledge representation, knowledge graphs, and semantic web technologies. The reader is assumed to have some knowledge of Swift but the book examples describe the parts of the Swift language that are used. This is not an introduction to Swift programming nor is it a broad general book on AI.

In order to make this book as broadly useful as possible for developers wanting to take a deep dive into NLP, deep learning, knowledge representation, and semantic web technologies all examples will be plain text console (command line) applications. Much of the code is useable on macOS (and iOS and iPadOS) and Linux except for the CoreML examples and Apple's NLP libraries that are Apple platforms only.

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Part 1: Introduction and Short Examples

We start this book with a sufficient introduction for Swift to understand the programming examples. After introducing the language we will look at a few short examples:

  • - Creating Swift Projects
  • - Writing command line utilities.
  • - Web scraping.

Part 2: Apple's CoreML and NLP Libraries

  • - Introduction of CoreML
  • - Examples using CoreML
  • - Introduction of NLP
  • - Examples using NLP libraries

Part 3: Knowledge Representation and Data Acquisition

  • - Introduction to the semantic web and linked data.
  • - A general discussion of Knowledge Representation with linked data
  • - Knowledge Graph Explorer application

Part 4: SwiftUI Example Applications

  • Knowledge Graph Explorer (uses SPARQL, deep learning models for NLP and question answering)

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
    • Parts of this Book are Specific for macOS and iOS, with Some Support for Linux
    • Code for this Book
    • Author’s Background
    • A Request from the Author
    • Cover Art
    • CoreML Libraries Used in this Book
    • Swift 3rd Party Libraries
    • Acknowledgements
  • Part 1: Introduction and Short Examples
  • Setting Up Swift for Command Line Development
    • Installing Swift Packages
    • Creating Swift Packages
    • Accessing Libraries that You Write in Other Projects
  • Background Information for Writing Swift Command Line Utilities
    • Using Shell Processes
    • FileIO Examples
    • Swift REPL
  • Web Scraping
    • Running in the Swift REPL
  • Part 2: Apple’s CoreML and NLP Libraries
  • Deep Learning Introduction
    • Simple Multi-layer Perceptron Neural Networks
    • Deep Learning
  • Natural Language Processing Using Apple’s Natural Language Framework
    • Using Apple’s NaturalLanguage Swift Library
    • A simple Wrapper Library for Apple’s NLP Models
  • Using the OpenAI APIs
  • Documents Question Answering Using OpenAI GPT3 APIs and a Local Embeddings Vector Database
    • Extending the String Class
    • Implementing a Local Vector Database for Document Embeddings
    • Create Local Embeddings Vectors From Local Text Files With OpenAI GPT APIs
    • Using Local Embeddings Vector Database With OpenAI GPT APIs
    • Wrap Up for Using Local Embeddings Vector Database to Enhance the Use of GPT3 APIs With Local Documents
  • Part 3: Knowledge Representation and Data Acquisition
  • Linked Data and the Semantic Web
    • Understanding the Resource Description Framework (RDF)
    • Frequently Used Resource Namespaces
    • Understanding the SPARQL Query Language
    • Semantic Web and Linked Data Wrap Up
  • Example Application: iOS and macOS Versions of my KnowledgeBookNavigator
    • Screen Shots of macOS Application
    • Application Code Listings
  • Book Wrap Up

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