Rust and machine learning #4: practical tools (Ep. 110) - a podcast by Francesco Gadaleta

from 2020-06-29T10:30

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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
  1. BLAS linear algebra https://en.wikipedia.org/wiki/Basic_Linear_Algebra_Subprograms

  2. Rust dataframe https://github.com/nevi-me/rust-dataframe

  3. Rustlearn https://github.com/maciejkula/rustlearn

  4. Rusty machine https://github.com/AtheMathmo/rusty-machine

  5. Tensorflow bindings https://lib.rs/crates/tensorflow

  6. Juice (machine learning for hackers) https://lib.rs/crates/juice

  7. Rust reinforcement learning https://lib.rs/crates/rsrl

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