Revisiting Keras for R
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 f…
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Train in R, run on Android: Image segmentation with torch
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, s…
Beyond alchemy: A first look at geometric deep learning
Geometric deep learning is a "program" that aspires to situate deep learning architectures and techniques in a framework of mathematical priors. The p…
torch: Just-in-time compilation (JIT) for R-less model deployment
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 u…
Starting to think about AI Fairness
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 proliferati…
sparklyr.sedona: A sparklyr extension for analyzing geospatial data
We are excited to announce the availability of sparklyr.sedona, a sparklyr extension making geospatial functionalities of the Apache Sedona library ea…
sparklyr 1.7: New data sources and spark_apply() capabilities, better interfaces for sparklyr extensions, and more!
Sparklyr 1.7 delivers much-anticipated improvements, including R interfaces for image and binary data sources, several new spark_apply() capabilities,…
Que haja luz: More light for torch!
Today, we're introducing luz, a high-level interface to torch that lets you train neural networks in a concise, declarative style. In some sense, it i…
torch for optimization
Torch is not just for deep learning. Its L-BFGS optimizer, complete with Strong-Wolfe line search, is a powerful tool in unconstrained as well as cons…
sparklyr 1.6: weighted quantile summaries, power iteration clustering, spark_write_rds(), and more
The sparklyr 1.6 release introduces weighted quantile summaries, an R interface to power iteration clustering, spark_write_rds(), as well as a number …
