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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

blog post

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

blog post

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

blog post

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

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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

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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

blog post

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

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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

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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

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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

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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

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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

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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

blog post

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

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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

blog post

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

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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

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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

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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

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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

blog post

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

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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

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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

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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

blog post

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

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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

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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

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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

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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

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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

blog post

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

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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

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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

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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

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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

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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

blog post

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

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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

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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

blog post

Getting familiar with torch tensors

In this first installment of a four-part miniseries, we present the main things you will want to know about torch tensors. As an illustrative example, we’ll code a simple neural network from scratch

Sigrid Keydana

Oct 1, 2020

Machine Learning Ai Torch

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Please allow me to introduce myself: Torch for R

Please allow me to introduce myself: Torch for R

Today, we are excited to introduce torch, an R package that allows you to use PyTorch-like functionality natively from R. No Python installation is required: torch is built directly on top of libtorch, a C++ library that provides the tensor-computation and automatic-differentiation capabilities essential to building neural networks

Sigrid Keydana

Sep 29, 2020

Machine Learning Ai Packages Releases Torch