Tensors and Neural Networks with 'GPU' Acceleration

Provides functionality to define and train neural networks similar to 'PyTorch' by Paszke et al (2019) but written entirely in R using the 'libtorch' library. Also supports low-level tensor operations and 'GPU' acceleration.


Reference manual

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0.6.0 by Daniel Falbel, 4 months ago

https://torch.mlverse.org/docs, https://github.com/mlverse/torch

Report a bug at https://github.com/mlverse/torch/issues

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

Authors: Daniel Falbel [aut, cre, cph] , Javier Luraschi [aut] , Dmitriy Selivanov [ctb] , Athos Damiani [ctb] , Christophe Regouby [ctb] , Krzysztof Joachimiak [ctb] , RStudio [cph]

Documentation:   PDF Manual  

Task views: Machine Learning & Statistical Learning

MIT + file LICENSE license

Imports Rcpp, R6, withr, rlang, methods, utils, stats, bit64, magrittr, tools, coro, callr, cli, ellipsis

Suggests testthat, covr, knitr, rmarkdown, glue, palmerpenguins, mvtnorm, numDeriv, katex

Linking to Rcpp

System requirements: C++11, LibTorch (https://pytorch.org/)

Imported by brulee, innsight, lambdaTS, luz, madgrad, proteus, scDHA, tabnet, topicmodels.etm, torchaudio, torchdatasets, torchvision.

Suggested by targets.

See at CRAN