Neural networks in Haskell - advice

Can anybody suggest me tutorial, book, blog or share code sample for neural networks in Hask开发者_JS百科ell ? I have experience in neural networks in imperative languages, but I want try that in Haskell.

There are several libraries on Hackage:

• HaskellNN A Haskell library which uses hmatrix (and, transitively, GSL and libLBFGS C libraries) to do heavy lifting (GPL). Claims to be fast.

• instinct A pure-Haskell library which claims to be fast (BSD).

• hnn A minimal Haskell Neural Network Library (LGPL).

• bindings-fann Bindings to FANN library.

• hfann Other bindings to FANN library.

You may find this sample application useful. It uses back-propagation. I wrote an article discussing the example, explaining how the use of a functional paradigm affects the design. The article should appear in the next issue of The Monad Reader.

The DataHaskell community keeps a more up-to-date list of Hackage packages at http://www.datahaskell.org/docs/community/current-environment.html#neural-networks

As of 2019-08-26, it recommends these packages:

• neural (CPU-only, see issue 10)
• backprop-learn uses the backprop library (CPU-only?)
• grenade (dependently typed! Comfortable API, but CPU-only so far, see issue 55 / issue 35 / issue 6)
• hasktorch (Haskell bindings to the C libs underlying PyTorch, early development but it should let you train on GPU)
• tensorflow (Haskell bindings to TF; most likely what you'd use in production, but intimidating API; can run on GPU)
• (and sibe (CPU-only), though under the ML heading, implements neural networks)

There's a series of blog posts on using TensorFlow from Haskell at https://mmhaskell.com/blog/2017/8/14/starting-out-with-haskell-tensor-flow / https://mmhaskell.com/blog/2017/8/21/digging-in-deep-solving-a-real-problem-with-haskell-tensor-flow etc.

If you're interested in autograd/differentiable programming, the backprop-learn author shows how to add dependent types to a neural network and how to do automatic differentiation (as in TF) from Haskell, which is what eventually turned into the backprop library. See also the ad library (quick demo here).