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-0.1.0.2.tar.gz [browse] (Cabal source package)
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| Versions [RSS] | 0.1.0.0, 0.1.0.1, 0.1.0.2 |
|---|---|
| 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 2023-05-13T12:43:14Z |
| Distributions | NixOS:0.1.0.2 |
| Downloads | 245 total (8 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] |