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This page summarizes the projects mentioned and recommended in the original post on reddit.com/r/rust

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  • GitHub repo linfa

    A Rust machine learning framework.

    for a start I will implement the TryFrom for Dataset under a feature flag. But to be really useful some of the algorithms have to start using something like DatasetBase here Records are currently bounded by an associated type for the element type, we would have to relax that too. Just read your blogpost on polars 👍

  • GitHub repo rust-ndarray

    ndarray: an N-dimensional array with array views, multidimensional slicing, and efficient operations

    well you can represent categorical values in `ndarray` for sure (even structured arrays [here](https://github.com/rust-ndarray/ndarray/issues/32)), but the memory has to be contiguous for BLAS/LAPACK and therefore it is impossible to mix continuous and categorical values. I was thinking that we could emulate categorical values with a descriptor field for the type of each feature and then just use floats to represent them.

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    Less time debugging, more time building. Scout APM allows you to find and fix performance issues with no hassle. Now with error monitoring and external services monitoring, Scout is a developer's best friend when it comes to application development.

  • GitHub repo Enzyme

    High-performance automatic differentiation of LLVM. (by wsmoses)

    this is definitely on our agenda, there are several autograd libraries on language level, which you can find on crates.io. The real game changer would be automatic differentiation of LLVM IR code, because we could then construct any order derivative without a special language construct. The Enzyme project provides the faculties and we currently trying to figure out how to integrate it into the ecosystem

NOTE: The number of mentions on this list indicates mentions on common posts plus user suggested alternatives. Hence, a higher number means a more popular project.

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