Category: Deep neural networks
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.
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.
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.
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.
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.
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.
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.
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.