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.


automl package 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.

(Key words: autoML, Deep Learning, Particle Swarm Optimization, learning rate, minibatch, batch normalization, lambda, RMSprop, momentum, adam optimization, learning rate decay, inverted dropout, particles number, kappa, regression, logistic regression)

Reference manual

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install.packages("automl")

1.3.2 by Alex Boulangé, 7 years ago


https://aboulaboul.github.io/automl https://github.com/aboulaboul/automl


Report a bug at https://github.com/aboulaboul/automl/issues


Browse source code at https://github.com/cran/automl


Authors: Alex Boulangé [aut, cre]


Documentation:   PDF Manual  


GNU General Public License license


Imports stats, utils, parallel

Suggests datasets


See at CRAN