inf-backprop: Automatic differentiation and backpropagation.

Automatic differentiation library with efficient reverse-mode backpropagation for Haskell.
This package provides a general-purpose automatic differentiation system designed for building strongly typed deep learning frameworks. It offers:
Reverse-mode automatic differentiation (backpropagation)
Support for higher-order derivatives
Type-safe gradient computation
Integration with numhask
Flexible representations including profunctor and Van Laarhoven encodings
The library emphasizes composability and type safety, making it suitable for research, prototyping neural networks, and implementing custom differentiable algorithms.
See the tutorial for detailed examples and usage patterns.
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- inf-backprop-0.2.0.0.tar.gz [browse] (Cabal source package)
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| Versions [RSS] | 0.1.0.0, 0.1.0.1, 0.1.0.2, 0.1.1.0, 0.2.0.0 |
|---|---|
| Change log | CHANGELOG.md |
| Dependencies | base (>=4.7 && <5), combinatorial (<0.2), comonad (<5.1), composition (<1.1), data-fix (<0.4), deepseq (<1.6), extra (<1.9), finite-typelits (<0.3), fixed-vector (<2.1), ghc-prim (<0.13), hashable (<1.6), indexed-list-literals (<0.3), isomorphism-class (<0.4), lens (<5.4), numhask (<0.14), optics (<0.5), primitive (<0.10), profunctors (<5.7), safe (<0.4), simple-expr (<0.3), Stream (<0.5), text (<2.2), transformers (<0.7), unordered-containers (<0.3), vector (<0.14), vector-sized (<1.7) [details] |
| License | BSD-3-Clause |
| Copyright | 2023-2025 Alexey Tochin |
| Author | Alexey Tochin |
| Maintainer | Alexey.Tochin@gmail.com |
| Category | Mathematics |
| Uploaded | by AlexeyTochin at 2025-11-19T23:22:02Z |
| Distributions | LTSHaskell:0.1.1.0, NixOS:0.1.1.0, Stackage:0.1.1.0 |
| Downloads | 360 total (16 in the last 30 days) |
| Rating | 2.0 (votes: 1) [estimated by Bayesian average] |
| Your Rating | |
| Status | Docs uploaded by user Build status unknown [no reports yet] |