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.
Category: Deep neural networks
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.
Community spotlight: Fun with torchopt
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...continue reading.
torch outside the box
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...continue reading.
Pre-processing layers in keras: What they are and how to use them
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...continue reading.
Keras for R is back!
For a while, it may have seemed that Keras for R was in some undecidable state, like Schrödinger’s cat before inspection. It is high time to correct that impression. Keras...continue reading.
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, so as to obtain an...continue reading.
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 priors, such as various types of invariance, first...continue reading.
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 use of the low-level libtorch...continue reading.
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 proliferation, abundance of formulae, and...continue reading.
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 easily accessible from R.continue reading.
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, and better integration with sparklyr extensions.continue reading.
