The topic of AI fairness metrics is as important to society as it is confusing. Confusing it is due to a number of reasons: terminological proliferation, abundance of formulae, and last not least the impression that everyone else seems to know what they’re talking about. This text hopes to counteract some of that confusion by […]
Using the torch just-in-time (JIT) compiler, it is possible to query a model trained in R from a different language, provided that language can make use of the low-level libtorch library. This post shows how. In addition, we try to untangle a bit of the terminological jumble surrounding the topic.
torch: Just-in-time compilation (JIT) for R-less model deployment Read More »
Geometric deep learning is a “program” that aspires to situate deep learning architectures and techniques in a framework of mathematical priors. The priors, such as various types of invariance, first arise in some physical domain. A neural network that well matches the domain will preserve as many invariances as possible. In this post, we present
Beyond alchemy: A first look at geometric deep learning Read More »
We train a model for image segmentation in R, using torch together with luz, its high-level interface. We then JIT-trace the model on example input, so as to obtain an optimized representation that can run with no R installed. Finally, we show the model being run on Android.
Train in R, run on Android: Image segmentation with torch Read More »
It’s been a while since this blog featured content about Keras for R, so you might’ve thought that the project was dormant. It’s not! In fact, Keras for R is better than ever, with two recent releases adding powerful capabilities that considerably lighten previously tedious tasks. This post provides a high-level overview. Future posts will
For keras, the last two releases have brought important new functionality, in terms of both low-level infrastructure and workflow enhancements. This post focuses on an outstanding example of the latter category: a new family of layers designed to help with pre-processing, data-augmentation, and feature-engineering tasks.
Pre-processing layers in keras: What they are and how to use them Read More »
Sometimes, a software’s best feature is the one you’ve added yourself. This post shows by example why you may want to extend torch, and how to proceed. It also explains a bit of what is going on in the background.
Today, we want to call attention to a highly useful package in the torch ecosystem: torchopt. It extends torch by providing a set of popular optimization algorithms not available in the base library. As this post will show, it is also fun to use!
Announcing the release of “Deep Learning with R, 2nd Edition,” a book that shows you how to get started with deep learning in R.
