Practical Python Artificial Intelligence Programming

Practical Python Artificial Intelligence Programming

Mark Watson
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Table of Contents

Practical Python Artificial Intelligence Programming

  • Cover Material, Copyright, and License
  • Preface
    • About the Author
    • Using the Example Code
    • Book Cover
    • Acknowledgements
  • Python Development Environment
    • Managing Python with uv
  • Part 1 - Machine Learning
  • “Classic” Machine Learning
    • Example Material
    • Classification Models using Scikit-learn
    • Classic Machine Learning Wrap-up
  • Regression and Clustering
    • Regression: Predicting Housing Prices
    • Clustering: Discovering Groups in Data
    • Regression and Clustering Wrap-up
  • Exploratory Data Analysis and Feature Engineering
    • Exploratory Data Analysis
    • Feature Engineering
    • EDA and Feature Engineering Wrap-up
  • Part 2 - Deep Learning
  • The Basics of Deep Learning
    • Using PyTorch for Building a Cancer Prediction Model
  • Natural Language Processing Using Deep Learning
    • Hugging Face and the Transformers Library
    • 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
    • Image Generation Using Stable Diffusion and PyTorch
    • Mini-DALL·E: A Lightweight Alternative
    • Recommended Reading for Image Generation
  • Overview of Reinforcement Learning (Optional Material)
    • Overview
    • Available RL Tools
    • An Introduction to Markov Decision Process
    • A Concrete Example: Q-Learning with Gymnasium
    • Reinforcement Learning Wrap-up
  • Overview of Recommendation Systems (Optional Material)
    • TensorFlow Recommenders
    • Recommendation Systems Wrap-up
  • Part 3 - Large Language Models
  • Introduction to Transformers and Large Language Models
    • The Transformer Architecture
    • Tokenization
    • From Transformers to Large Language Models
    • Key Capabilities of Modern LLMs
    • Practical Considerations
  • LLMs with Public APIs
    • Setup and Authentication
    • Text Generation
    • Thinking Models
    • Multi-Turn Conversations
    • Multimodal Input: Analyzing Images
    • Web Search with LLMs
    • Structured Output
    • Practical Considerations
    • Summary
  • LLMs with Local Models
    • Installing Ollama
    • Downloading and Running Models
    • Using Ollama from Python
    • Reasoning with Local Models
    • Conversation Memory with Ollama
    • Prompt Caching for Performance
    • OpenAI-Compatible API
    • Alternative Tools for Running Local Models
    • Hardware Considerations
    • Summary
  • Part 4 - Symbolic AI and Knowledge Representation
  • 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 5 - Knowledge Representation
  • Getting Setup To Use Graph and Relational Databases
    • Querying Wikidata with SPARQL and Python
    • The SQLite Relational Database for Knowledge Representation
  • Optional Material: A Deeper Dive Into Semantic Web and Linked Data
    • Overview and Theory
Practical Python Artificial Intelligence Programming/overview

Practical Python Artificial Intelligence Programming

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Artificial Intelligence programming with Python. Covers both theory and practical examples of LLMs, deep learning, symbolic AI, and Knowledge Representation

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Practical Python Artificial Intelligence Programming23 chapters

Begin ›
  1. Cover Material, Copyright, and License

  2. Preface

  3. Python Development Environment

  4. Part 1 - Machine Learning

  5. “Classic” Machine Learning

  6. Regression and Clustering

  7. Exploratory Data Analysis and Feature Engineering

  8. Part 2 - Deep Learning

  9. The Basics of Deep Learning

  10. Natural Language Processing Using Deep Learning

  11. Part IV - Overviews of Image Generation, Reinforcement Learning, and Recommendation Systems

  12. Overview of Image Generation

  13. Overview of Reinforcement Learning (Optional Material)

  14. Overview of Recommendation Systems (Optional Material)

  15. Part 3 - Large Language Models

  16. Introduction to Transformers and Large Language Models

  17. LLMs with Public APIs

  18. LLMs with Local Models

  19. Part 4 - Symbolic AI and Knowledge Representation

  20. Symbolic AI

  21. Part 5 - Knowledge Representation

  22. Getting Setup To Use Graph and Relational Databases

  23. Optional Material: A Deeper Dive Into Semantic Web and Linked Data