1. Getting Started

RustyML is a machine learning and deep learning library written entirely in Rust: no BLAS to link, no Python runtime, no C++ bindings.

Prerequisites. You should be comfortable with cargo, Rust’s ownership and borrowing, and the basic supervised loop (train, predict, evaluate). No prior ndarray experience is required — 1.3 covers the part of it you need. Everything runs on Rust 1.89 or newer, with no system libraries to install.

1.1 What is RustyML: a short tour of what RustyML offers (classical ML estimators, a Keras-style neural-network stack, preprocessing, evaluation metrics), and an honest account of the trade-offs pure Rust brings.

1.2 Installation and Feature Flags: what each feature means and which ones you should turn on.

1.3 Working with ndarray: a short introduction to ndarray. Every matrix RustyML takes in or hands back is an ndarray type, so knowing how to use ndarray is unavoidable.

1.4 Your First End-to-End Model: building a complete model pipeline with RustyML, hands on.

1.5 The Prelude and Imports: how RustyML’s prelude modules let you pull names into scope quickly.

1.6 Error Handling: what RustyML’s error type is, and how you should handle failures.