In mlr-org/mlr3torch: Deep Learning with 'mlr3'

The table below shows all preprocessing and augmentation operations that are available in mlr3torch.

library(data.table)
library(mlr3torch)
content = as.data.table(mlr3pipelines::mlr_pipeops)
content$packages = lapply(content$packages, function(ps) setdiff(ps, c("mlr3torch", "mlr3pipelines")))
mlr3torch_ids = names(getFromNamespace("mlr3torch_pipeops", "mlr3torch"))
content = content[key %in% mlr3torch_ids & grepl("^(trafo|augment)", key)]
content$key = sapply(content$key, function(key) sprintf("[%s](https://mlr3torch.mlr-org.com/reference/mlr_pipeops_%s.html)", key, key))
content = content[, .(key, label, packages, feature_types)]
names(content) = c("Key", "Label", "Packages", "Feature Types")
knitr::kable(content)