Artificial Intelligence Using Swift
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.
All of my Leanpub books, including this book, can be read for FREE on my web site: https://markwatson.com/
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
Table of Contents
- Cover Material, Copyright, and License
-
Preface
- Notes on the Second Edition
- Book Structure
- 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: Large Language Models
-
Using the OpenAI LLM APIs
- Core Architecture
- Key Features
- Technical Implementation Details
- Running Tests
-
Using APIs for Anthropic Claude LLMs
- Running the examples
-
Using Groq APIs to Open Weight LLM Models
- Implementation of a Client Library for the Groq APIs
- Running the Tests
-
Using the xAI Grok LLM
- Implementation of a Grok API Client Library
-
Using Ollama to Run Local LLMs
- Running the Ollama Service
- Ollama Wrap Up
-
Using Apple’s MLX Framework to Run Local LLMs
- MLX Framework History
- MLX Resources on GitHub
- Example Application for MLX Swift Examples Repository
- Analysis of Swift and SwiftUI Code in the LLMEval Application
- Part 3: 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
-
Documents Question Answering Using OpenAI GPT4 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 4: 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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