In this episode I make a non exhaustive list of machine learning tools and frameworks, written in Rust. Not all of them are mature enough for production environments. I believe that community effort can change this very quickly.
To make a comparison with the Python ecosystem I will cover frameworks for linear algebra (numpy), dataframes (pandas), off-the-shelf machine learning (scikit-learn), deep learning (tensorflow) and reinforcement learning (openAI).
Rust is the language of the future. Happy coding!
References
- BLAS linear algebra https://en.wikipedia.org/wiki/Basic_Linear_Algebra_Subprograms
- Rust dataframe https://github.com/nevi-me/rust-dataframe
- Rustlearn https://github.com/maciejkula/rustlearn
- Rusty machine https://github.com/AtheMathmo/rusty-machine
- Tensorflow bindings https://lib.rs/crates/tensorflow
- Juice (machine learning for hackers) https://lib.rs/crates/juice
- Rust reinforcement learning https://lib.rs/crates/rsrl
