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torch v0.10.0 is now on CRAN. This version upgraded the underlying LibTorch to 1.13.1, and added support for Automatic Mixed Precision. As an experimental feature, we now also support pre-built binaries, so you can install torch without having to deal with the CUDA installation.
luz v0.4.0 is now on CRAN. This release adds support for training models on ARM Mac GPUs, reduces the overhead of using luz, and makes it easier to checkpoint and resume failed runs.
Escnn, built on PyTorch, is a library that, in the spirit of Geometric Deep Learning, provides a high-level interface to designing and training group-equivariant neural networks. This post introduces important mathematical concepts, the library’s key actors, and essential library use.
torch v0.11.0 is now on CRAN. This release features much-enhanced support for executing JIT operations. We also amended loading of model parameters, and added a few quality-of-life improvements, like support for temporarily modifying the default torch device, support for specifying data types as strings, and many more.
Implementing a language model from scratch is, arguably, the best way to develop an accurate idea of how its engine works. Here, we use torch to code GPT-2, the immediate successor to the original GPT. In the end, you’ll dispose of an R-native model that can make direct use of Hugging Face’s pre-trained GPT-2 model
LoRA (Low Rank Adaptation) is a new technique for fine-tuning deep learning models that works by reducing the number of trainable parameters and enables efficient task switching. In this blog post we will talk about the key ideas behind LoRA in a very minimal torch example.
Hugging Face rapidly became a very popular platform to build, share and collaborate on deep learning applications. We have worked on integrating the torch for R ecosystem with Hugging Face tools, allowing users to load and execute language models from their platform.
We code up a simple group-equivariant convolutional neural network (GCNN) that is equivariant to rotation. The world may be upside down, but the network will know.
Implementing rotation equivariance: Group-equivariant CNN from scratch Read More »
We conclude our mini-series on time-series forecasting with torch by augmenting last time’s sequence-to-sequence architecture with a technique both immensely popular in natural language processing and inspired by human (and animal) cognition: attention.
Please allow us to introduce Deep Learning and Scientific Computing with R torch. Released in e-book format today, and available freely online, this book starts out by introducing torch basics. From there, it moves on to various deep-learning use cases. Finally, it shows how to use torch for more general topics, such as matrix computations
Deep Learning and Scientific Computing with R torch: the book Read More »
