Making a better Tensorflow thanks to strong typing

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  • gamma

    Computational graphs with reverse automatic differentation in the GPU

  • niura

    Automatic differentiation in pure Rust.

    i just released my own auto-diff library called niura, (it's unstable and unsafe at the moment) and i've been looking for a simple, rust-compatible way to do gpu acceleration for matrix-multiplication, could you recommend something in that regard?

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  • arrayfire-rust

    Rust wrapper for ArrayFire

    Take a look at arrayfire-rust! :)

  • juice

    The Hacker's Machine Learning Engine (by spearow)

    how does it compare with, and

  • neuronika

    Tensors and dynamic neural networks in pure Rust.

    how does it compare with, and

  • rust

    Rust language bindings for TensorFlow (by tensorflow)

    What is the benefit of this compared to using bindings/a wrapper to Tensorflow, or other ML libraries written in C/C++, such as this community hosted project on tensorflow's github. If it's just for fun that is a valid enough reason imo, just curious since you describe it as a better Tensorflow because of the typing vs using the python wrapper, when there already exist ways to interact with tensorflow with both Rust and other statically typed languages, also including C++ (officially supported), C#, Haskell and Scala, as well as probably having bindings not mentioned on the documentation for more niche languages.

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