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deepdive
View on CRAN: Click
here
Download and install deepdive package within the R console
Install from CRAN:
install.packages("deepdive")
Install from Github:
library("remotes")
install_github("cran/deepdive")
Install by package version:
library("remotes")
install_version("deepdive", "1.0.4")
Attach the package and use:
library("deepdive")
Maintained by
Rajesh Balakirshnan
[Scholar Profile | Author Map]
[Scholar Profile | Author Map]
All associated links for this package
10.32614/CRAN.package.deepdive . https://rajeshb24.github.io/deepdive/ . deepdive results . deepdive.pdf . deepdive_1.0.4.tar.gz . deepdive_1.0.4.zip . deepdive_1.0.4.zip . deepdive_1.0.4.zip . deepdive_1.0.4.tgz . deepdive_1.0.4.tgz . deepdive_1.0.4.tgz . deepdive_1.0.4.tgz . deepdive_1.0.4.tgz . deepdive_1.0.4.tgz . deepdive archive . https://CRAN.R-project.org/package=deepdive .
First Published: 2020-03-26
Latest Update: 2021-07-10
Description:
Aims to provide simple intuitive functions to create quick prototypes of artificial neural network or deep learning models. In addition novel ensemble models like 'deeptree' and 'deepforest' has been included which combines decision trees and neural network.
How to cite:
Rajesh Balakirshnan (2020). deepdive: Deep Learning for General Purpose. R package version 1.0.4, https://cran.r-project.org/web/packages/deepdive. Accessed 29 Mar. 2025.
Previous versions and publish date:
Other packages that cited deepdive R package
View deepdive citation profile
Other R packages that deepdive depends,
imports, suggests or enhances
Complete documentation for deepdive
Functions, R codes and Examples using
the deepdive R package
Some associated functions: deepforest . deepnet . deeptree . predict.deepforest . predict.deepnet . predict.deeptree . variableImportance .
Some associated R codes: backProp.R . deepForest.R . deepnet.R . deeptree.R . feedForward.R . miniBatchCreate.R . multiActivationList.R . multiDataCut.R . multiLayerIntialiser.R . multiVariableCut.R . optimiserAdjust.R . predict.deepforest.R . predict.deepnet.R . predict.deeptree.R . shuffler.R . variableImportance.R . weightIntialiser.R . weightUpdate.R . weightUpdateInitialiser.R . Full deepdive package functions and examples
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