CRAN/E | nnR

nnR

Neural Networks Made Algebraic

Installation

About

Do algebraic operations on neural networks. We seek here to implement in R, operations on neural networks and their resulting approximations. Our operations derive their descriptions mainly from Rafi S., Padgett, J.L., and Nakarmi, U. (2024), "Towards an Algebraic Framework For Approximating Functions Using Neural Network Polynomials", doi:10.48550/arXiv.2402.01058, Grohs P., Hornung, F., Jentzen, A. et al. (2023), "Space-time error estimates for deep neural network approximations for differential equations", doi:10.1007/s10444-022-09970-2, Jentzen A., Kuckuck B., von Wurstemberger, P. (2023), "Mathematical Introduction to Deep Learning Methods, Implementations, and Theory" doi:10.48550/arXiv.2310.20360. Our implementation is meant mainly as a pedagogical tool, and proof of concept. Faster implementations with deeper vectorizations may be made in future versions.

github.com/2shakilrafi/nnR/
Bug report File report

Key Metrics

Version 0.1.0
R ≥ 4.1.0
Published 2024-02-14 236 days ago
Needs compilation? no
License GPL-3
CRAN checks nnR results

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Maintainer

Maintainer

Shakil Rafi

Authors

Shakil Rafi

aut / cre

Joshua Lee Padgett

aut

Ukash Nakarmi

ctb

Material

Reference manual
Package source

Vignettes

nnR

macOS

r-release

arm64

r-oldrel

arm64

r-release

x86_64

Windows

r-develnot available

x86_64

r-releasenot available

x86_64

r-oldrelnot available

x86_64

Depends

R ≥ 4.1.0

Suggests

knitr
rmarkdown
testthat ≥ 3.0.0