Welcome!

Welcome to Rustineers, a dive into the Rust programming language through the lens of applied mathematics and science. There are already several high-quality resources available for learning Rust:

• The Book – a comprehensive introduction to Rust.
• Rustlings – hands-on exercises to reinforce learning.
• Rust by Example – learn by studying runnable examples.
• Rust Language Cheat Sheet - really useful when you're trying to remember something without asking your favorite LLM.

You can find even more learning material at rust-lang.org.

This book is meant to be complementary to those great resources. Our goal is to learn Rust by implementing practical examples drawn from applied mathematics, including:

• Machine Learning
• Statistics and Probability
• Optimization
• Ordinary Differential Equations (ODEs)
• Partial Differential Equations (PDEs)
• And other topics from engineering and physics

Each chapter centers around a specific scientific algorithm or computational problem. We explore how to implement it idiomatically in Rust and sometimes in multiple styles.

Hopefully, we manage to go through the core concepts of Rust, namely:

• Ownership / borrowing
• Data types
• Traits
• Modules
• Error handling
• Macros

Most examples being with Rust's standard library which seems to be a solid foundation for learning both the language and its ecosystem.

Difficulty Levels

To help you navigate the material, each chapter is marked with a difficulty level using 🦀 emojis:

• 🦀 — Beginner-friendly
• 🦀🦀 — Intermediate
• 🦀🦀🦀 — Advanced

As this is a work in progress, the difficulty levels might not always be well chosen.

Roadmap

Here's an unordered list of examples of topics that could be added to the book:

• 1D Ridge regression.
• Simple first-order gradient descent algorithms.
• Multivariate regression algorithms (Ridge, Lasso, Elastic-net).
• Classification algorithms: logistic regression.
• Some clustering algorithms: K-means, Gaussian mixtures.
• Some MCMC algorithms: MH, LMC, MALA.
• Numerical methods for solving ODEs: Euler, Runge-Kutta.
• Numerical methods for solving PDEs: 1D heat equation, 2D Poisson equation.
• Optimization algorithms: gradient-based, derivative-free.
• Kernel methods: kernel Ridge, Gaussian processes, etc.
• Divergences and distances for probability distributions: KL divergence, total variation, Wasserstein.

Let us know if you have other ideas or if you want to improve any existing chapter.