This repository is a collection of educational Python programs and Jupyter notebooks by Peter Norvig, focused on algorithmic problem-solving, puzzle-solving, and data analysis. It is not a deployable application or a formal research pipeline, but rather a set of self-contained explorations that demonstrate a wide range of programming techniques: constraint propagation, recursive search, dynamic programming, Monte Carlo simulation, n-gram language models, and symbolic computation. The code is well-structured, clearly documented, and often includes inline tests and performance benchmarks. While each notebook or script is independent, many contain reusable utility functions and algorithmic patterns that could be extracted for other projects. The repository includes data files (word lists, n-gram counts, bike ride logs, Sudoku puzzles) that are used as inputs for the explorations. The primary value is educational: it teaches how to think algorithmically and write clean, efficient Python code.
This repository is primarily an extensive educational compendium covering mathematics, computer science, and artificial intelligence from first principles to state-of-the-art topics. It contains over 100 markdown files organized into 20 chapters, each with detailed explanations, diagrams, and interactive coding tasks (primarily in Python/JAX). The repo also includes auxiliary tooling: a MkDocs-based documentation site with MathJax and a Pyodide in-browser Python runner that includes a JAX compatibility shim, and a TypeScript MCP (Model Context Protocol) server that provides programmatic access to the compendium content for AI assistants. While the vast majority of the repository is educational content rather than runnable software, the MCP server and interactive runner are functional application components. The compendium is well-structured and current, but the code examples are pedagogical (simplified, not production-ready) and the JAX shim is a fragile polyfill. The project is not a data research pipeline—it contains no datasets, experiment logs, or trained models.