Package: automl 1.3.2

automl: Deep Learning with Metaheuristic

Fits from simple regression to highly customizable deep neural networks either with gradient descent or metaheuristic, using automatic hyper parameters tuning and custom cost function. A mix inspired by the common tricks on Deep Learning and Particle Swarm Optimization.

Authors:Alex Boulangé [aut, cre]

automl_1.3.2.tar.gz
automl_1.3.2.zip(r-4.7-any)automl_1.3.2.zip(r-4.6-any)automl_1.3.2.zip(r-4.5-any)
automl_1.3.2.tgz(r-4.6-any)automl_1.3.2.tgz(r-4.5-any)
automl_1.3.2.tar.gz(r-4.7-any)automl_1.3.2.tar.gz(r-4.6-any)
automl_1.3.2.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
automl/json (API)

# Install 'automl' in R:

install.packages('automl', repos = c('https://aboulaboul.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/aboulaboul/automl/issues

Pkgdown/docs site:https://aboulaboul.github.io

On CRAN:

5.65 score 28 stars 32 scripts 465 downloads 3 exports 0 dependencies

Last updated from:55b1ad7285. Checks:9 OK. Indexed: yes.

Target	Result	Time
linux-devel	OK	102
source / vignettes	OK	131
linux-release	OK	83
macos-release	OK	111
macos-oldrel	OK	140
windows-devel	OK	60
windows-release	OK	53
windows-oldrel	OK	59
wasm-release	OK	68

Exports:automl_predict automl_train automl_train_manual

Dependencies:

howto_automl.pdf

Rendered fromhowto_automl.Rnwusingutils::Sweave

Last update: 2019-09-11
Started: 2018-10-21

Citation

Development and contributors

Readme and manuals

Help Manual

Help page	Topics
automl_predict	automl_predict
automl_train	automl_train
automl_train_manual	automl_train_manual
parameters for automatic hyperparameters optimization	autopar
Deep Neural Net parameters and hyperparameters	hpar
PSO parameters and hyperparameters	pso