inf-backprop: Automatic differentiation and backpropagation.

Automatic differentiation and backpropagation.
We do not attract gradient tape.
Instead, the differentiation operator is defined directly as a map between differentiable function objects.
Such functions are to be combined in arrow style using (>>>), (***), first, etc.
The original purpose of the package is an automatic backpropagation differentiation component for a functional type-dependent library for deep machine learning. See tutorial details.
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- inf-backprop-0.1.1.0.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 | 
|---|---|
| Change log | CHANGELOG.md | 
| Dependencies | base (>=4.7 && <5), comonad, isomorphism-class, monad-logger, numhask, simple-expr, text, transformers [details] | 
| License | BSD-3-Clause | 
| Copyright | 2023 Alexey Tochin | 
| Author | Alexey Tochin | 
| Maintainer | Alexey.Tochin@gmail.com | 
| Category | Mathematics | 
| Uploaded | by AlexeyTochin at 2025-02-06T00:42:43Z | 
| Distributions | LTSHaskell:0.1.1.0, NixOS:0.1.1.0, Stackage:0.1.1.0 | 
| Downloads | 348 total (11 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] |