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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 […]
Discrete Fourier Transform – with torch Read More »
torch v0.9.0 is now on CRAN. This version adds support for ARM systems running macOS, and brings significant performance improvements.
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
Wavelet Transform – with torch Read More »
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
AO, NAO, ENSO: A wavelet analysis example Read More »
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.
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
torch time series, take three: Sequence-to-sequence prediction Read More »
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.
Please allow me to introduce myself: Torch for R Read More »
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.
Getting familiar with torch tensors Read More »
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.
Introducing torch autograd Read More »
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.
Using torch modules Read More »
