torch 0.11.0
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...continue reading.
Category: Deep neural networks
LLaMA in R with Keras and TensorFlow
Implementation and walk-through of LLaMA, a Large Language Model, in R, with TensorFlow and Keras.continue reading.
Group-equivariant neural networks with escnn
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...continue reading.
luz 0.4.0
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...continue reading.
torch 0.10.0
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...continue reading.
De-noising Diffusion with torch
Currently, in generative deep learning, no other approach seems to outperform the family of diffusion models. Would you like to try for yourself? If so, our torch implementation of de-noising...continue reading.
Deep Learning and Scientific Computing with R torch: the book
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....continue reading.
Implementing rotation equivariance: Group-equivariant CNN from scratch
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.continue reading.
Upside down, a cat’s still a cat: Evolving image recognition with Geometric Deep Learning
In this first in a series of posts on group-equivariant convolutional neural networks (GCNNs), meet the main actors — groups — and concepts (equivariance). With GCNNs, we finally revisit the...continue reading.
AO, NAO, ENSO: A wavelet analysis example
El Niño-Southern Oscillation (ENSO), North Atlantic Oscillation (NAO), and Arctic Oscillation (AO) are atmospheric phenomena of global impact that strongly affect people’s lives. ENSO, first and foremost, brings with it...continue reading.
Wavelet Transform – with torch
torch does not have built-in functionality to do wavelet analysis. But we can efficiently implement what we need, making use of the Fast Fourier Transform (FFT). This post is a...continue reading.
torch 0.9.0
torch v0.9.0 is now on CRAN. This version adds support for ARM systems running macOS, and brings significant performance improvements.continue reading.
Discrete Fourier Transform – with torch
About the Fourier Transform, it has been said that it is one of the greatest wonders of the universe. At the same time, it can be realized in a mere...continue reading.
Five ways to do least squares (with torch)
Get to know torch’s linalg module, all while learning about different ways to do least-squares regression from scratch. This post is a condensed version of the corresponding chapter in the...continue reading.
Audio classification with torch
Learn how to classify speech utterances with torch, making use of domain knowledge and deep learning. This post is a condensed version of the corresponding chapter in the forthcoming book,...continue reading.
Introducing the text package
The text package attempts to provide user-friendly access and pipelines to HuggingFace’s transformer language models in R.continue reading.
luz 0.3.0
luz version 0.3.0 is now on CRAN. luz is a high-level interface for torch.continue reading.
TensorFlow and Keras 2.9
New TensorFlow and Keras releases bring improvements big and small.continue reading.
Deep Learning with R, 2nd Edition
Announcing the release of “Deep Learning with R, 2nd Edition,” a book that shows you how to get started with deep learning in R.continue reading.
Deep Learning with R, 2nd Edition
Announcing the release of “Deep Learning with R, 2nd Edition”, a book that shows you how to get started with deep learning in R.continue reading.
