Making a better Tensorflow thanks to strong typing

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

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  • gamma

    Computational graphs with reverse automatic differentation in the GPU

  • niura

    Discontinued 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?

  • InfluxDB

    Power Real-Time Data Analytics at Scale. Get real-time insights from all types of time series data with InfluxDB. Ingest, query, and analyze billions of data points in real-time with unbounded cardinality.

    InfluxDB logo
  • arrayfire-rust

    Rust wrapper for ArrayFire

  • Take a look at arrayfire-rust! :)

  • juice

    The Hacker's Machine Learning Engine (by fff-rs)

  • how does it compare with https://github.com/spearow/juice, https://github.com/neuronika/neuronika and https://github.com/spearow/juice?

  • neuronika

    Tensors and dynamic neural networks in pure Rust.

  • how does it compare with https://github.com/spearow/juice, https://github.com/neuronika/neuronika and https://github.com/spearow/juice?

  • 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.

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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