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.
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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.
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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.
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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.
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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.
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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.
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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!
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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.
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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.
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