Are we learning 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.

Being an early stage ecosystem, there are plenty of opportunities for contributors to fill in the gaps by helping out existing projects or starting new ones.

For help or questions with Rust ML, reach out on #science-and-ai (Discord) or #rust-machine-learning (IRC).

Additional resources

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

• The PlayRust Classifier - Real world data science to build r/rust vs. r/playrust classifier
• Spam or Ham? - End-to-end Rust ML tutorial aimed at Rust beginners (uses rustlearn)
• Learning Machines - Interactive tutorials on machine learning powered by rusty-machine
• About Rust's ML Community - An assessment of Rust ML from Jan. 2016
• Rusty Machine talk - Intro to Rust ML with rusty-machine
• ...suggest additional resources for getting started with ML in Rust