Haskell deep learning tutorials [Blog]

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

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  • tensorflow

    Haskell bindings for TensorFlow

    First I still have to admit that Haskell ML libraries have a large space for improvement. On the other hand, there exists bindings for Tensorflow. As can be seen from Github, they have recently added support for libtensorflow v2.3.0.

  • accelerate

    Embedded language for high-performance array computations (by AccelerateHS)

    Backprop is a neat library. However, I guess its use case is if you actually don't want to go for anything standard like Torch or TF (perhaps for research?) For instance, if I were to use something like Accelerate for GPU acceleration, or some other computation-oriented library, then I would mix it with Backprop. Previously, I have benefited from Backprop in a ConvNet tutorial and I liked it.

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  • hasktorch

    Tensors and neural networks in Haskell

    As rightfully pointed u/gelisam, both Hasktorch and Pytorch are essentially the same things (bindings to existing Torch library). Therefore, it should be generally possible to use existing pretrained models. Here is an example.

  • grenade

    Deep Learning in Haskell

    Grenade is fun, but it does not support CUDA, so it will limit you. I would say that this was a great experiment that has influenced the Hasktorch library in different ways (let me know if I am wrong).

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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