Auto Added by WPeMatico
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 is to torch what Keras is to TensorFlow: It provides both a streamlined workflow and powerful ways for customization.
Que haja luz: More light for torch! Read More »
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 forthcoming book, Deep Learning and Scientific Computing with R torch, to be published by CRC Press.
Five ways to do least squares (with torch) Read More »
Sparklyr 1.7 delivers much-anticipated improvements, including R interfaces for image and binary data sources, several new spark_apply() capabilities, and better integration with sparklyr extensions.
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 half-dozen lines of code. Even if in the end, you’re just going to call torch’s built-in functions directly, it helps to understand, and be able
Discrete Fourier Transform – with torch Read More »
We are excited to announce the availability of sparklyr.sedona, a sparklyr extension making geospatial functionalities of the Apache Sedona library easily accessible from R.
sparklyr.sedona: A sparklyr extension for analyzing geospatial data Read More »
torch v0.9.0 is now on CRAN. This version adds support for ARM systems running macOS, and brings significant performance improvements.
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
Starting to think about AI Fairness Read More »
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 very first introduction to wavelets, suitable for readers that have not encountered it before. At the same time, it provides useful starter code, showing an
Wavelet Transform – with torch Read More »
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 »
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 floods, droughts, and ensuing poverty, in developing countries in the Southern Hemisphere. Here, we use the new torchwavelets package to comparatively inspect patterns in the
AO, NAO, ENSO: A wavelet analysis example Read More »
