Rust and machine learning #4: practical tools (Ep. 110)

Episode 107 of the Data Science at Home podcast, hosted by Francesco Gadaleta, titled "Rust and machine learning #4: practical tools (Ep. 110)" was published on June 29, 2020 and runs 24 minutes.

June 29, 2020 ·24m · Data Science at Home

Summary

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!  Reference 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

Episode Description