Practical Python Artificial Intelligence Programming

Cover Material, Copyright, and License

Preface

1. About the Author
2. Using the Example Code
3. Book Cover
4. Acknowledgements

Python Development Environment

1. Managing Python with uv

Part 1 - Machine Learning

“Classic” Machine Learning

1. Example Material
2. Classification Models using Scikit-learn
3. Classic Machine Learning Wrap-up

Regression and Clustering

1. Regression: Predicting Housing Prices
2. Clustering: Discovering Groups in Data
3. Regression and Clustering Wrap-up

Exploratory Data Analysis and Feature Engineering

1. Exploratory Data Analysis
2. Feature Engineering
3. EDA and Feature Engineering Wrap-up

Anomaly Detection

1. The Wisconsin Breast Cancer Dataset
2. Data Preprocessing
3. Approach 1: Gaussian Statistical Detector
4. Approach 2: Isolation Forest
5. Running the Example
6. Interpreting the Results
7. Anomaly Detection Wrap-up

Part 2 - Deep Learning

The Basics of Deep Learning

1. Using PyTorch for Building a Cancer Prediction Model

Natural Language Processing Using Deep Learning

1. Hugging Face and the Transformers Library
2. Comparing Sentences for Similarity Using Transformer Models
3. Deep Learning Natural Language Processing Wrap-up

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

Overview of Image Generation

1. Image Generation Using Stable Diffusion and PyTorch
2. Mini-DALL·E: A Lightweight Alternative
3. Recommended Reading for Image Generation

Overview of Reinforcement Learning (Optional Material)

1. Overview
2. Available RL Tools
3. An Introduction to Markov Decision Process
4. A Concrete Example: Q-Learning with Gymnasium
5. Reinforcement Learning Wrap-up

Overview of Recommendation Systems (Optional Material)

1. TensorFlow Recommenders
2. Recommendation Systems Wrap-up

Part 3 - Large Language Models

Introduction to Transformers and Large Language Models

1. The Transformer Architecture
2. Tokenization
3. From Transformers to Large Language Models
4. Key Capabilities of Modern LLMs
5. Practical Considerations

LLMs with Public APIs

1. Setup and Authentication
2. Text Generation
3. Thinking Models
4. Multi-Turn Conversations
5. Multimodal Input: Analyzing Images
6. Web Search with LLMs
7. Structured Output
8. Practical Considerations
9. Summary

LLMs with Local Models

1. Installing Ollama
2. Downloading and Running Models
3. Using Ollama from Python
4. Reasoning with Local Models
5. Conversation Memory with Ollama
6. Prompt Caching for Performance
7. OpenAI-Compatible API
8. Alternative Tools for Running Local Models
9. Hardware Considerations
10. Summary

Part 4 - Symbolic AI and Knowledge Representation

Symbolic AI

1. Comparison of Symbolic AI and Deep Learning
2. Implementing Frame Data Structures in Python
3. Use Predicate Logic by Calling Swi-Prolog
4. Swi-Prolog and Python Deep Learning Interop
5. Soar Cognitive Architecture
6. Constraint Programming with MiniZinc and Python
7. Good Old Fashioned Symbolic AI Wrap-up

Part 5 - Knowledge Representation

Getting Setup To Use Graph and Relational Databases

1. Querying Wikidata with SPARQL and Python
2. The SQLite Relational Database for Knowledge Representation

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

1. Overview and Theory