Haskell deep learning tutorials [Blog]

• tensorflow

  2 1,583 0.0 Haskell

  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.

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• accelerate

  10 907 7.1 Haskell

  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.

• hasktorch

  15 1,079 8.3 Haskell

  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

  5 1,450 5.6 Haskell

  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).

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