neuralnetwork: Fast Compact Multilayer Perceptrons
A small multilayer perceptron implementation for
'R'. It supports regression and classification, multiple hidden layers,
mini-batch training, adaptive moment estimation, stochastic gradient descent,
momentum, Nesterov acceleration, resilient backpropagation and limited-memory
quasi-Newton optimization, dropout, squared-weight regularization, early
stopping, convergence thresholds, gradient clipping, sample and class weights,
callback hooks,
target scaling and robust Huber loss for regression, 'Rcpp' forward-pass
kernels, formula interfaces, model evaluation with balanced classification
metrics, cross-validation, compact tuning, permutation importance, model
persistence helpers, and standard prediction methods. Methods follow
Rumelhart, Hinton and Williams (1986) <doi:10.1038/323533a0>, with
optimizers including Riedmiller and Braun (1993)
<doi:10.1109/ICNN.1993.298623>, Nocedal (1980)
<doi:10.1090/S0025-5718-1980-0572855-7>, and Kingma and Ba (2014)
<doi:10.48550/arXiv.1412.6980>.
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