Question 1

How do I install HNN in my Haskell project?

Accepted Answer

You can install HNN via Cabal or Stack by adding it as a dependency in your .cabal file or package.yaml. Detailed installation instructions are available on Hackage, as mentioned in the README.

Question 2

HNN vs TensorFlow: which is better for Haskell?

Accepted Answer

HNN is a pure Haskell library focused on type-safe feed-forward networks, ideal for functional programming enthusiasts. TensorFlow offers Haskell bindings but is more extensive; for complex models outside FFNs, TensorFlow might be preferable.

Question 3

Can I use HNN for deep learning?

Accepted Answer

HNN supports deep feed-forward networks with multiple layers, but it lacks specialized layers for convolutional or recurrent networks, limiting its applicability to standard deep learning tasks.

Question 4

What activation functions does HNN support?

Accepted Answer

While not explicitly listed, HNN likely includes common activation functions like sigmoid, tanh, and ReLU for feed-forward networks. Check the `AI.HNN.FF.Network` module tutorial for specifics.

Question 5

How to train a neural network with HNN?

Accepted Answer

Follow the tutorial in the `AI.HNN.FF.Network` module to construct networks, define loss functions, and use automatic differentiation for training via backpropagation, as outlined in the examples.

Question 6

Is HNN good for production machine learning?

Accepted Answer

HNN is suitable for production within Haskell ecosystems for feed-forward models, but its limited architecture support and smaller community may pose challenges for scaling or maintaining complex ML systems.

hnn

What is hnn?

Overview

Use Cases

Best For

Related Projects

Found a gem we're missing?

Not Ideal For

Pros & Cons

Pros

Cons

Frequently Asked Questions