neural: Neural Networks in native Haskell

The goal of neural is to provide a modular and flexible neural network library written in native Haskell.

Features include

• composability via Arrow instances and pipes,

• automatic differentiation for automatic gradient descent/ backpropagation training (using Edward Kmett's fabulous ad library).

The idea is to be able to easily define new components and wire them up in flexible, possibly complicated ways (convolutional deep networks etc.).

Two examples are included as proof of concept:

• A simple neural network that approximates the sqrt function on [0,4].

• A slightly more complicated neural network that solves the famous Iris flower problem.

The library is still very much experimental at this point.

Modules

[Index]

Downloads

• neural-0.1.0.1.tar.gz [browse] (Cabal source package)
• Package description (revised from the package)

Note: This package has metadata revisions in the cabal description newer than included in the tarball. To unpack the package including the revisions, use 'cabal get'.

Maintainer's Corner

Package maintainers

• lbrunjes

For package maintainers and hackage trustees

• edit package information

Candidates

• 0.1.0.0, 0.1.0.1, 0.1.0.2, 0.1.1.0, 0.2.0.0, 0.3.0.0