Are we learning yet?

A work-in-progress to catalog
          the state of machine learning in Rust

It's ripe for experimentation,
but the ecosystem isn't very complete yet.

Rust's performance, low-level control, and zero-cost high-level abstractions make it a compelling alternative to more established ecosystems for Machine Learning. While the Rust ML ecosystem is still young and best described as experimental, several ambitious projects and building blocks have emerged. Using Rust to solve a real-world machine learning problem even made the cut for RustConf 2016.

In addition to the many wonderful projects that contain individual implementations of certain methods and algorithms (many of which are listed here), there are also meta-crates like linfa and smartcore that provide a convenient, bundled approach to many machine learning algorithms that may be of interest to developers looking for a practical solution

As a developing ecosystem, there are plenty of opportunities for contributors to fill in the gaps by helping out existing projects or starting new ones.

An unofficial Working Group in the machine learning domain has been established here. For help or questions with Rust ML, feel free to reach out on the Zulip chat.

Additional resources

See also some of these talks, tutorials, demos about machine learning in Rust: