BROADCASTS.com

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.\nTo 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).\nRust is the language of the future.Happy coding!\xa0\nReference\nBLAS linear algebra https://en.wikipedia.org/wiki/Basic_Linear_Algebra_Subprograms\nRust dataframe\xa0https://github.com/nevi-me/rust-dataframe\nRustlearn https://github.com/maciejkula/rustlearn\nRusty machine https://github.com/AtheMathmo/rusty-machine\nTensorflow bindings https://lib.rs/crates/tensorflow\nJuice (machine learning for hackers) https://lib.rs/crates/juice\nRust reinforcement learning https://lib.rs/crates/rsrl