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AI blog posts

The AI blog has a new home#

The AI blog has moved to the Posit Open Source blog . All past and future AI posts now live here alongside posts from our other open-source projects.

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mall 0.2.0

The mall 0.2.0 update for R and Python introduces support for external LLM providers like OpenAI and Gemini. This version also features parallel processing for R users, the ability to run NLP on string vectors in Python, and a brand new cheatsheet

Edgar Ruiz

Aug 19, 2025

Artificial Intelligence Machine Learning Ai LLM Polars Natural Language Processing Tabular Data

Introducing mall for R…and Python

We are proud to introduce the {mall} package. With {mall}, you can use a local LLM to run NLP operations across a data frame. (sentiment, summarization, translation, etc). {mall} has been simultaneously released to CRAN and PyPi (as an extension to Polars)

Edgar Ruiz

Oct 30, 2024

Artificial Intelligence Machine Learning Ai LLM Polars Natural Language Processing Tabular Data

Introducing Keras 3 for R

We are thrilled to introduce {keras3}, the next version of the Keras R package. {keras3} is a ground-up rebuild of {keras}, maintaining the beloved features of the original while refining and simplifying the API based on valuable insights gathered over the past few years

Tomasz Kalinowski

May 21, 2024

keras3 Machine Learning Ai Tensorflow Keras

News from the sparkly-verse

Highlights to the most recent updates to sparklyr and friends

Edgar Ruiz

Apr 22, 2024

MLOps and Admin Data Wrangling Ai Packages Releases Spark

Chat with AI in RStudio

Interact with Github Copilot and OpenAI’s GPT (ChatGPT) models directly in RStudio. The chattr Shiny add-in makes it easy for you to interact with these and other Large Language Models (LLMs)

Edgar Ruiz

Apr 4, 2024

rstudio Artificial Intelligence Ai Generative Models Packages Releases

Hugging Face Integrations

Hugging Face rapidly became a very popular platform to build, share and collaborate on deep learning applications. We have worked on integrating the torch for R ecosystem with Hugging Face tools, allowing users to load and execute language models from their platform

Daniel Falbel

Jul 12, 2023

Machine Learning Ai Torch Releases

Understanding LoRA with a minimal example

Understanding LoRA with a minimal example

LoRA (Low Rank Adaptation) is a new technique for fine-tuning deep learning models that works by reducing the number of trainable parameters and enables efficient task switching. In this blog post we will talk about the key ideas behind LoRA in a very minimal torch example

Daniel Falbel

Jun 22, 2023

Machine Learning Best Practices Ai Torch Concepts

What are Large Language Models? What are they not?

What are Large Language Models? What are they not?

This is a high-level, introductory article about Large Language Models (LLMs), the core technology that enables the much-en-vogue chatbots as well as other Natural Language Processing (NLP) applications. It is directed at a general audience, possibly with some technical and/or scientific background, but no knowledge is assumed of either deep learning or NLP. Having looked at major model ingredients, training workflow, and mechanics of output generation, we also talk about what these models are not

Sigrid Keydana

Jun 20, 2023

Best Practices Artificial Intelligence Ai Meta Concepts Natural Language Processing

GPT-2 from scratch with torch

Implementing a language model from scratch is, arguably, the best way to develop an accurate idea of how its engine works. Here, we use torch to code GPT-2, the immediate successor to the original GPT. In the end, you’ll dispose of an R-native model that can make direct use of Hugging Face’s pre-trained GPT-2 model weights

Sigrid Keydana

Jun 20, 2023

Machine Learning Artificial Intelligence Ai Torch Natural Language Processing

safetensors 0.1.0

Announcing safetensors, a new R package allowing for reading and writing files in the safetensors format

Daniel Falbel

Jun 15, 2023

Machine Learning Ai Packages Releases

torch 0.11.0

torch v0.11.0 is now on CRAN. This release features much-enhanced support for executing JIT operations. We also amended loading of model parameters, and added a few quality-of-life improvements, like support for temporarily modifying the default torch device, support for specifying data types as strings, and many more

Daniel Falbel

Jun 7, 2023

Machine Learning Ai Torch Packages Releases

LLaMA in R with Keras and TensorFlow

Implementation and walk-through of LLaMA, a Large Language Model, in R, with TensorFlow and Keras

Tomasz Kalinowski

May 25, 2023

keras3 Machine Learning Artificial Intelligence Ai Tensorflow Keras Generative Models Natural Language Processing

Group-equivariant neural networks with escnn

Group-equivariant neural networks with escnn

Escnn, built on PyTorch, is a library that, in the spirit of Geometric Deep Learning, provides a high-level interface to designing and training group-equivariant neural networks. This post introduces important mathematical concepts, the library’s key actors, and essential library use

Sigrid Keydana

May 9, 2023

Machine Learning Best Practices Ai Torch Concepts Image Recognition Image Processing

luz 0.4.0

luz v0.4.0 is now on CRAN. This release adds support for training models on ARM Mac GPUs, reduces the overhead of using luz, and makes it easier to checkpoint and resume failed runs

Daniel Falbel

Apr 17, 2023

Machine Learning Ai Torch Packages Releases

torch 0.10.0

torch v0.10.0 is now on CRAN. This version upgraded the underlying LibTorch to 1.13.1, and added support for Automatic Mixed Precision. As an experimental feature, we now also support pre-built binaries, so you can install torch without having to deal with the CUDA installation

Daniel Falbel

Apr 14, 2023

Machine Learning Ai Torch Packages Releases

De-noising Diffusion with torch

Currently, in generative deep learning, no other approach seems to outperform the family of diffusion models. Would you like to try for yourself? If so, our torch implementation of de-noising diffusion provides an easy-to-use, easy-to-configure interface

Daniel Falbel, Sigrid Keydana

Apr 13, 2023

Machine Learning Ai Torch Image Recognition Image Processing Generative Models

Deep Learning and Scientific Computing with R torch: the book

Deep Learning and Scientific Computing with R torch: the book

Please allow us to introduce Deep Learning and Scientific Computing with R torch. Released in e-book format today, and available freely online, this book starts out by introducing torch basics. From there, it moves on to various deep-learning use cases. Finally, it shows how to use torch for more general topics, such as matrix computations and the Fourier Transform

Sigrid Keydana

Apr 5, 2023

Machine Learning Best Practices Ai Torch Meta Concepts Packages Releases

Implementing rotation equivariance: Group-equivariant CNN from scratch

Implementing rotation equivariance: Group-equivariant CNN from scratch

We code up a simple group-equivariant convolutional neural network (GCNN) that is equivariant to rotation. The world may be upside down, but the network will know

Sigrid Keydana

Mar 27, 2023

Machine Learning Ai Torch Spatial Data Image Recognition Image Processing

Upside down, a cat’s still a cat: Evolving image recognition with Geometric Deep Learning

In this first in a series of posts on group-equivariant convolutional neural networks (GCNNs), meet the main actors — groups — and concepts (equivariance). With GCNNs, we finally revisit the topic of Geometric Deep Learning, a principled, math-driven approach to neural networks that has consistently been rising in scope and impact

Sigrid Keydana

Mar 9, 2023

Machine Learning Best Practices Ai Torch Spatial Data Concepts

AO, NAO, ENSO: A wavelet analysis example

AO, NAO, ENSO: A wavelet analysis example

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 three series

Sigrid Keydana

Jan 19, 2023

Machine Learning Ai Torch Packages Releases Time Series

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 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 (extensible) way to perform wavelet analysis in torch. It is an excerpt from the corresponding chapter in the forthcoming book, Deep Learning and Scientific Computing with R torch, to be published by CRC Press

Sigrid Keydana

Oct 27, 2022

Machine Learning Best Practices Ai Torch Concepts

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

Daniel Falbel

Oct 25, 2022

Machine Learning Ai Torch Packages Releases

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 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 to reproduce in code, the ideas that underlie the magic. This post is an excerpt from the forthcoming book, Deep Learning and Scientific Computing with R torch, to be published by CRC Press

Sigrid Keydana

Oct 20, 2022

Machine Learning Best Practices Ai Torch Concepts

Five ways to do least squares (with torch)

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 forthcoming book, Deep Learning and Scientific Computing with R torch, to be published by CRC Press

Sigrid Keydana

Oct 13, 2022

Machine Learning Best Practices Ai Torch Concepts Tabular Data

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, Deep Learning and Scientific Computing with R torch, to be published by CRC Press

Sigrid Keydana

Oct 6, 2022

Machine Learning Ai Torch Audio Processing

Introducing the text package

The text package attempts to provide user-friendly access and pipelines to HuggingFace’s transformer language models in R

Oscar Kjell, Salvatore Giorgi, H Andrew Schwartz

Oct 4, 2022

Machine Learning Ai Natural Language Processing

luz 0.3.0

luz version 0.3.0 is now on CRAN. luz is a high-level interface for torch

Daniel Falbel

Aug 24, 2022

Machine Learning Ai Torch Packages Releases

TensorFlow and Keras 2.9

New TensorFlow and Keras releases bring improvements big and small

Tomasz Kalinowski

Jun 9, 2022

keras3 Machine Learning Ai Tensorflow Keras Packages Releases

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

Tomasz Kalinowski

May 31, 2022

keras3 Machine Learning Ai Tensorflow Keras

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 the base library. As this post will show, it is also fun to use!

Sigrid Keydana

May 18, 2022

Machine Learning Ai Torch Packages Releases

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 a bit of what is going on in the background

Sigrid Keydana

Apr 27, 2022

Machine Learning Ai Torch

Pre-processing layers in keras: What they are and how to use them

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 category: a new family of layers designed to help with pre-processing, data-augmentation, and feature-engineering tasks

Sigrid Keydana, Tomasz Kalinowski

Dec 9, 2021

keras3 Machine Learning Ai Tensorflow Keras

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 for R is better than ever, with two recent releases adding powerful capabilities that considerably lighten previously tedious tasks. This post provides a high-level overview. Future posts will go into more detail on some of the most helpful new features, as well as dive into the powerful low-level enhancements that make the former possible

Sigrid Keydana, Tomasz Kalinowski

Nov 18, 2021

keras3 Machine Learning Ai Packages Releases Tensorflow Keras

Train in R, run on Android: Image segmentation with torch

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 optimized representation that can run with no R installed. Finally, we show the model being run on Android

Sigrid Keydana

Oct 29, 2021

Machine Learning Ai Torch Image Recognition Image Processing

Beyond alchemy: A first look at geometric deep learning

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 arise in some physical domain. A neural network that well matches the domain will preserve as many invariances as possible. In this post, we present a very conceptual, high-level overview, and highlight a few applications

Sigrid Keydana

Aug 26, 2021

Best Practices Ai Concepts Meta

torch: Just-in-time compilation (JIT) for R-less model deployment

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 library. This post shows how. In addition, we try to untangle a bit of the terminological jumble surrounding the topic

Sigrid Keydana

Aug 10, 2021

Machine Learning Ai Torch

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 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 from a common-sense approach of contrasting two basic positions: On the one hand, the assumption that dataset features may be taken as reflecting the underlying concepts ML practitioners are interested in; on the other, that there inevitably is a gap between concept and measurement, a gap that may be bigger or smaller depending on what is being measured. In contrasting these fundamental views, we bring together concepts from ML, legal science, and political philosophy

Sigrid Keydana

Jul 15, 2021

Best Practices Ai Concepts Meta Society

sparklyr.sedona: A sparklyr extension for analyzing geospatial data

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

Yitao Li

Jul 7, 2021

MLOps and Admin Ai Packages Releases Distributed Computing Spatial Data

sparklyr 1.7: New data sources and spark_apply() capabilities, better interfaces for sparklyr extensions, and more!

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

Yitao Li

Jul 6, 2021

MLOps and Admin Ai Packages Releases Distributed Computing

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 to TensorFlow: It provides both a streamlined workflow and powerful ways for customization

Sigrid Keydana

Jun 17, 2021

Machine Learning Ai Torch Packages Releases

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

Sigrid Keydana

Apr 27, 2021

Machine Learning Ai Torch

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 of dplyr-related improvements

Yitao Li

Mar 25, 2021

MLOps and Admin Ai Packages Releases Distributed Computing

torch time series, final episode: Attention

torch time series, final episode: Attention

We conclude our mini-series on time-series forecasting with torch by augmenting last time’s sequence-to-sequence architecture with a technique both immensely popular in natural language processing and inspired by human (and animal) cognition: attention

Sigrid Keydana

Mar 19, 2021

Machine Learning Ai Torch Time Series

torch time series, take three: Sequence-to-sequence prediction

torch time series, take three: Sequence-to-sequence prediction

In our overview of techniques for time-series forecasting, we move on to sequence-to-sequence models. Architectures in this family are commonly used in natural language processing (NLP) tasks, such as machine translation. With NLP, however, significant pre-processing is required before proceeding to model definition and training. In staying with our familiar numerical series, we can fully concentrate on the concepts

Sigrid Keydana

Mar 16, 2021

Machine Learning Ai Torch Time Series

torch time series continued: A first go at multi-step prediction

torch time series continued: A first go at multi-step prediction

We continue our exploration of time-series forecasting with torch, moving on to architectures designed for multi-step prediction. Here, we augment the “workhorse RNN” by a multi-layer perceptron (MLP) to extrapolate multiple timesteps into the future

Sigrid Keydana

Mar 11, 2021

Machine Learning Ai Torch Time Series

Introductory time-series forecasting with torch

Introductory time-series forecasting with torch

This post is an introduction to time-series forecasting with torch. Central topics are data input, and practical usage of RNNs (GRUs/LSTMs). Upcoming posts will build on this, and introduce increasingly involved architectures

Sigrid Keydana

Mar 10, 2021

Machine Learning Ai Torch Time Series

First mlverse survey results – software, applications, and beyond

First mlverse survey results – software, applications, and beyond

Last month, we conducted our first survey on mlverse software, covering topics ranging from area of application through software usage to user wishes and suggestions. In addition, the survey asked about thoughts on social impacts of AI/ML. This post presents the results, and tries to address some of the things that came up

Sigrid Keydana

Feb 17, 2021

Community Ai Meta

torch, tidymodels, and high-energy physics

torch, tidymodels, and high-energy physics

Today we introduce tabnet, a torch implementation of “TabNet: Attentive Interpretable Tabular Learning” that is fully integrated with the tidymodels framework. Per se, already, tabnet was designed to require very little data pre-processing; thanks to tidymodels, hyperparameter tuning (so often cumbersome in deep learning) becomes convenient and even, fun!

Sigrid Keydana

Feb 11, 2021

tidymodels Machine Learning Ai Torch Tabular Data

Simple audio classification with torch

This article translates Daniel Falbel’s post on “Simple Audio Classification” from TensorFlow/Keras to torch/torchaudio

Athos Damiani

Feb 4, 2021

Machine Learning Ai Torch Audio Processing

Forecasting El Niño-Southern Oscillation (ENSO)

Forecasting El Niño-Southern Oscillation (ENSO)

El Niño-Southern Oscillation (ENSO) is an atmospheric phenomenon, located in the tropical Pacific, that greatly affects ecosystems as well as human well-being on a large portion of the globe. We use the convLSTM introduced in a prior post to predict the Niño 3.4 Index from spatially-ordered sequences of sea surface temperatures

Sigrid Keydana

Feb 2, 2021

Machine Learning Ai Torch Image Recognition Image Processing Time Series Spatial Data

Convolutional LSTM for spatial forecasting

Convolutional LSTM for spatial forecasting

In forecasting spatially-determined phenomena (the weather, say, or the next frame in a movie), we want to model temporal evolution, ideally using recurrence relations. At the same time, we’d like to efficiently extract spatial features, something that is normally done with convolutional filters. Ideally then, we’d have at our disposal an architecture that is both recurrent and convolutional. In this post, we build a convolutional LSTM with torch

Sigrid Keydana

Dec 17, 2020

Machine Learning Ai Torch Image Recognition Image Processing Time Series Spatial Data

torch 0.2.0 - Initial JIT support and many bug fixes

torch 0.2.0 - Initial JIT support and many bug fixes

The torch 0.2.0 release includes many bug fixes and some nice new features like initial JIT support, multi-worker dataloaders, new optimizers and a new print method for nn_modules

Daniel Falbel

Dec 15, 2020

Machine Learning Ai Torch Packages Releases

A hollywood star with the text 'sparklyr 1.5'

sparklyr 1.5: better dplyr interface, more sdf_* functions, and RDS-based serialization routines

Unlike all three previous sparklyr releases, the recent release of sparklyr 1.5 placed much more emphasis on enhancing existing sparklyr features rather than creating new ones. As a result, many valuable suggestions from sparklyr users were taken into account and were successfully addressed in a long list of bug fixes and improvements

Yitao Li

Dec 14, 2020

MLOps and Admin Ai Packages Releases Distributed Computing

Brain image segmentation with torch

The need to segment images arises in various sciences and their applications, many of which are vital to human (and animal) life. In this introductory post, we train a U-Net to mark lesioned regions on MRI brain scans

Sigrid Keydana

Nov 30, 2020

Machine Learning Ai Torch Image Recognition Image Processing

torch for tabular data

How not to die from poisonous mushrooms. Also: How to use torch for deep learning on tabular data, including a mix of categorical and numerical features

Sigrid Keydana

Nov 3, 2020

Machine Learning Ai Torch Tabular Data

Classifying images with torch

We learn about transfer learning, input pipelines, and learning rate schedulers, all while using torch to tell apart species of beautiful birds

Sigrid Keydana

Oct 19, 2020

Machine Learning Ai Torch Image Recognition Image Processing

sparklyr.flint 0.2: ASOF Joins, OLS Regression, and additional summarizers

sparklyr.flint 0.2: ASOF Joins, OLS Regression, and additional summarizers

We are excited to announce a number of powerful, new functionalities and improvements which are now part of sparklyr.flint 0.2!

Yitao Li

Oct 12, 2020

Machine Learning Ai Packages Releases Time Series

Optimizers in torch

Today, we wrap up our mini-series on torch basics, adding to our toolset two abstractions: loss functions and optimizers

Sigrid Keydana

Oct 9, 2020

Machine Learning Ai Torch

Using torch modules

In this third installment of our mini-series introducing torch basics, we replace hand-coded matrix operations by modules, considerably simplifying our toy network’s code

Sigrid Keydana

Oct 7, 2020

Machine Learning Ai Torch

Introducing torch autograd

With torch, there is hardly ever a reason to code backpropagation from scratch. Its automatic differentiation feature, called autograd, keeps track of operations that need their gradients computed, as well as how to compute them. In this second post of a four-part series, we update our simple, hand-coded network to make use of autograd

Sigrid Keydana

Oct 5, 2020

Machine Learning Ai Torch

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