TensorFlow and Keras 2.9
New TensorFlow and Keras releases bring improvements big and small.continue reading.
Category: Deep neural networks
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.
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 is to torch what Keras is...continue reading.
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 constrained optimization.continue reading.
sparklyr 1.6: weighted quantile summaries, power iteration clustering, spark_write_rds(), and more
Sparklyr 1.6 is now available on CRAN! To install sparklyr 1.6 from CRAN, run install.packages("sparklyr") In this blog post, we shall highlight the following features and enhancements from sparklyr 1.6:...continue reading.
torch time series, final episode: Attention
This is the final post in a four-part introduction to time-series forecasting with torch. These posts have been the story of a quest for multiple-step prediction, and by now, we’ve...continue reading.
torch time series, take three: Sequence-to-sequence prediction
Today, we continue our exploration of multi-step time-series forecasting with torch. This post is the third in a series. Initially, we covered basics of recurrent neural networks (RNNs), and trained...continue reading.
torch time series continued: A first go at multi-step prediction
We pick up where the first post in this series left us: confronting the task of multi-step time-series forecasting. Our first attempt was a workaround of sorts. The model had...continue reading.
Introductory time series forecasting with torch
This is the first post in a series introducing time-series forecasting with torch. It does assume some prior experience with torch and/or deep learning. But as far as time series...continue reading.
