If you’re new to torch, deep learning, or both, we recommend checking out our comprehensive book Deep Learning and Scientific Computing with R torch, available both online and in print form from CRC Press. It covers the basics (tensors, autograd, modules…), then works through popular deep-learning applications such as image segmentation, regression on tabular data, sound classification, and time-series forecasting. (And there’s more – see below.)
Still on the topic on basics and neural networks, for more practice with torch , you might want to give our learnr tutorials a try.
Deep learning is not the only thing, however, you can do with torch. The aforementioned book, in its third part, uses torch for least-squares solution via matrix decomposition, to compute the Discrete Fourier Transform, and for the purpose of wavelet analysis.
Finally, to stay on top of new developments and releases, follow the Posit AI Blog. There, you’ll also find interesting case studies and exciting new contributions from the community.