HNN
A Haskell Neural Network library for deep learning.
Pricing
Free tier
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Open Source
Data freshness
Aging · Jun 8, 2026Overview
What is HNN?
HNN is a Haskell-based neural network library that provides tools and functionalities to build and train deep learning models. It's particularly useful for developers interested in leveraging Haskell's strong typing and functional programming features for machine learning tasks.
Key differentiator
“HNN stands out as one of the few neural network libraries that fully integrates with Haskell, offering developers a functional programming approach to machine learning.”
Capability profile
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Strengths & Weaknesses
↑ Strengths
↓ Weaknesses
HNN is a niche library within the Haskell ecosystem, which has a smaller developer base compared to more mainstream deep learning frameworks like TensorFlow or PyTorch.
While Haskell offers strong typing and functional programming benefits, it may not match the performance of lower-level languages commonly used in deep learning such as C++ or Python with optimized libraries like NumPy.
HNN's integration capabilities are limited compared to more established frameworks, which have extensive support for various data sources, cloud services, and visualization tools.
Fit analysis
Who is it for?
✓ Best for
Developers who prefer functional programming for building neural networks
Teams working on Haskell projects that require integration with machine learning models
✕ Not a fit for
Projects requiring real-time performance critical applications where Haskell might not be the best choice
Users looking for a more mature and widely adopted deep learning framework like TensorFlow or PyTorch
Cost structure
Pricing
Free Tier
Available
Open source — free to use
Starts at
$0
Model
Flat rate
Enterprise
None
Performance benchmarks
How Fast Is It?
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Next step
Get Started with HNN
Step-by-step setup guide with code examples and common gotchas.