PyTorch

…Participants use PyTorch to build and train models on high-end GPUs through an HPC cluster, connecting from their own computers to the Austrian Scientific Computing JupyterHub. Temporary cluster accounts support the exercises, and a short pre-assignment prepares participants before the course. The English-language course is intended for software developers, data scientists and researchers moving from

…and graph-neural-network methods in PyTorch. Practical work turns molecular and omics data into model inputs, implements a graph model from a scientific paper, and examines disease classification and target discovery. The final day builds pipelines from research databases and introduces language-model fine-tuning using biological data. Live online, 30 November–3 Decemb

…parameterization, Fortran, Python and PyTorch. Appointment: 6 months; advertised start 2026-12-01. Starting from €2,695 gros

…and DNA-sequence models in JAX or PyTorch, improves distributed training and inference, and develops research libraries and evaluation tools.

Quantifying behavior is crucial for many applications in neuroscience. Videography provides easy methods for the observation and recording of animal behavior in diverse settings, yet extracting particular aspects of a behavior for further analysis can be highly time consuming. DeepLabCut was the first tool to innovate in this space, and since the initial publication (Mathis et al. Nature Neuroscience 2018) has been downloaded more than half a million times. While the code-base was actively developed, after 6 years it is time for a major release. Here we present DeepLabCut 3.0, which builds on

…integrates with the existing PyTorch ecosystem using abstractions familiar to the machine learning community. This has immediate benefits in that it provides a familiar interface, hardware accelerator support and, most importantly, the ability to use gradient-based optimization. While many parallel efforts in this direction exist, Norse emphasizes flexibility and usability in three ways. Use

…Norse expands PyTorch with primitives for bio-inspired neural components, bringing you two advantages: a modern and proven infrastructure based on PyTorch and deep learning-compatible spiking neural network components.

In this workshop you'll learn the fundamentals of PyTorch using an incremental, from-first-principles approach. We'll start with tensors, autograd, and the dynamic computation graph, and then move on to developing and training a simple model using PyTorch's model classes, datasets, data loaders, optimizers, and more. You should be comfortable using Python, Jupyter notebooks, Google Colab, Numpy and, preferably, object oriented programming.