The hardware and bandwidth for this mirror is donated by dogado GmbH, the Webhosting and Full Service-Cloud Provider. Check out our Wordpress Tutorial.
If you wish to report a bug, or if you are interested in having us mirror your free-software or open-source project, please feel free to contact us at mirror[@]dogado.de.

kindling (development version)

kindling 0.2.0

New features

• Added regularization support for neural network models

  • L1 regularization (Lasso) for feature selection via mixture = 1
  • L2 regularization (Ridge) for weight decay via mixture = 0
  • Elastic Net combining L1 and L2 penalties via 0 < mixture < 1
  • Controlled via penalty (regularization strength) and mixture (L1/L2 balance) parameters
  • Follows tidymodels conventions for consistency with glmnet and other packages

• n_hlayers() now fully supports tuning the number of hidden layers

• hidden_neurons() gains support for discrete values via the disc_values argument

  • e.g. disc_values = c(32L, 64L, 128L, 256L)) is now allowed
  • This allows tuning over specific common hidden unit sizes instead of (or in addition to) a continuous range

Implementation fixes

• Tuning methods and grid_depth() is now fixed

  • Parameter space for the number of hidden layers is now fixed and active
  • Corrected parameter space handling for n_hlayers (no more invalid sampling when x > 1)
  • Uses tidyr::expand_grid(), not purrr::cross*()
  • Fix randomization of parameter space which will produce NAs outside from {kindling}‘s own ’dials’
  • No more list columns when n_hlayers = 1

• The supported models now use hardhat::mold(), instead of model.frame() and model.matrix().

Documentation

• Add a vignette to showcase the comparison with other similar packages

• The package description got few clarifications

• Vignette to showcase the comparison with other similar packages

• hidden_neurons parameter now supports discrete values specification

  • Users can specify exact neuron counts via values parameter (e.g., hidden_neurons(values = c(32, 64, 128)))
  • Maintains backward compatibility with range-based parameters (e.g., hidden_neurons(range = c(8L, 512L)) / hidden_neurons(c(8L, 512L)))

• Added \value documentation to kindling-nn-wrappers for CRAN compliance

• Documented argument handling and list-column unwrapping in tidymodels wrapper functions

• Clarified the relationship between grid_depth() and wrapper functions

kindling 0.1.0

• Initial CRAN release
• Higher-level interface for torch package to define, train, and tune neural networks
• Support for feedforward (multi-layer perceptron) and recurrent networks (RNN, LSTM, GRU)
• Integration with tidymodels ecosystem (parsnip, workflows, recipes, tuning)
• Variable importance plots and network visualization tools

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.