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

<p>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.</p> <p>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).</p> <p>Rust is the language of the future. Happy coding! </p> Reference <ol><li><a href='https://en.wikipedia.org/wiki/Basic_Linear_Algebra_Subprograms'>BLAS linear algebra https://en.wikipedia.org/wiki/Basic_Linear_Algebra_Subprograms</a></li> <li><a href='https://github.com/nevi-me/rust-dataframe'>Rust dataframe https://github.com/nevi-me/rust-dataframe</a></li> <li><a href='https://github.com/maciejkula/rustlearn'>Rustlearn https://github.com/maciejkula/rustlearn</a></li> <li><a href='https://github.com/AtheMathmo/rusty-machine'>Rusty machine https://github.com/AtheMathmo/rusty-machine</a></li> <li><a href='https://lib.rs/crates/tensorflow'>Tensorflow bindings https://lib.rs/crates/tensorflow</a></li> <li><a href='https://lib.rs/crates/juice'>Juice (machine learning for hackers) https://lib.rs/crates/juice</a></li> <li><a href='https://lib.rs/crates/rsrl'>Rust reinforcement learning https://lib.rs/crates/rsrl</a></li> </ol>