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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)

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Install

install.packages('automl')

Monthly Downloads

261

Version

1.3.2

License

GNU General Public License

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Maintainer

Alex Boulangc3<a9>

Last Published

January 16th, 2020

Functions in automl (1.3.2)

autopar

parameters for automatic hyperparameters optimization
automl_train

automl_train
pso

PSO parameters and hyperparameters
hpar

Deep Neural Net parameters and hyperparameters
automl_predict

automl_predict
automl_train_manual

automl_train_manual