Show and tell by example of ENCCS/deep-learning-intro Rendered result https://carpentries-lab.github.io/deep-learning-intro/ https://enccs.github.io/deep-learning-intro/ Goals: fork, but stay in sync with upstream Assumptions: Carpentries episodes are static md, not executed Rmd

==EVITA = EuroHPC Virtual Training Academy== "What will be delivered" is rather clear Project duration: 4 years Partners: BSC, POLIMI, TUW, LiU, UniLu, USTUTT, IT4I@VSB, GWDG The big picture: :::info "How" is where your input matters

Newsletter

==Presented by: Ashwin Mohanan, Josefina Algotsson, Juan Sanchez Nieto, Pierre Meyrat, Valter Fallenius, Heiner Körnich== Nearly all meteorological agencies in the world, including SMHI, possesses troves of archived observations spanning decades in paper format. Dawsonia is a proof of concept application which combines accurate computer vision algorithms and machine learning models to handle different forms of tabular data, convert handwritten text and produce machine-readable files. This would aid and accelerate the digitization work from the paper archives into data, which is done manually as of now. As a result of the project, SMHI aims at digitizing numerous historical weather observations that will help a better understanding of the climate, especially of the occurrence of extreme weather events. The method implemented in Dawsonia is presented along with the development process. We also describe how the machine learning models were trained on LUMI, an EuroHPC supercomputer with technical support from ENCCS. Repository: https://git.smhi.se/ai-for-obs/dawsonia Documentation: https://dawsonia.readthedocs.io/ Demo: https://hf.co/spaces/ai-for-obs/dawsonia-demo

Practical Intro to GPU Programming using Python :::success Oct. 24, 12:00 - 13:30 (CET), 2024 ::: General information :::info Links for ENCCS