The ‘torch for R’ ecosystem is a collection of extensions for torch, an R framework for machine learning and artificial intelligence based on PyTorch.
Torch for R… wow! 🔥🔥🔥 I was recently having a discussion with a coworker about the benefits of Torch, especially the power of training one global model capable of hierarchical projections (awesome for time series) and predicting multiple group-specific regressions. I went down a Googling rabbit hole last weekend and came across some amazing articles by Sigrid Keydana (see links below) introducing torch to the R community and also recently releasing luz, a high-level R interface to Torch (“luz is to torch what Keras is to TensorFlow”).
RStudio’s MLVerse team is doing really exciting things for the R machine learning and AI community. With torch, I no longer need to launch a conda environment for complex NNs (although having Python on your system is always handy 😅). And even better, “torch for R is built directly on top of libtorch, a C++ library that provides the tensor-computation and automatic-differentiation capabilities essential to building neural networks.” If you’re looking for fast NNs and deep learning solutions within the #rstats framework, give these packages a try. Happy Friday and happy learning! 🤓📚
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For attribution, please cite this work as
Orraca (2021, Oct. 22). Javier Orraca: Torch for R + luz. Retrieved from https://www.javierorraca.com/posts/2021-10-21-torch-for-r/
BibTeX citation
@misc{orraca2021torch,
author = {Orraca, Javier},
title = {Javier Orraca: Torch for R + luz},
url = {https://www.javierorraca.com/posts/2021-10-21-torch-for-r/},
year = {2021}
}