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001033605 1001_ $$0P:(DE-Juel1)170068$$aSchiffer, Christian$$b0$$ufzj
001033605 1112_ $$aINM Retreat 2024$$cJülich$$d2024-11-19 - 2024-11-19$$wGermany
001033605 245__ $$aTutorial: Deep Learning for Neuroscience
001033605 260__ $$c2024
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001033605 520__ $$aMachine Learning – in particular deep learning – has become an indispensable tool for analyzing large neuroscience datasets. The Helmholtz AI team at Jülich is closely connected to these developments and supports research activities at the intersection of AI, high-performance computing (HPC) and neuroscience. Many of the methods and solutions are not limited to neuroscience and medical applications, but can be transferred to different tasks and scientific domains.This tutorial we will give an overview of state-of-the-art deep learning methods in the context of biomedical image analysis and show concrete examples in INM where deep learning already supports neuroscientists in analyzing their data. The second part of this tutorial will offer a hands-on course on how to bring deep learning pipelines on JSC’s HPC systems.
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001033605 536__ $$0G:(DE-HGF)InterLabs-0015$$aHIBALL - Helmholtz International BigBrain Analytics and Learning Laboratory (HIBALL) (InterLabs-0015)$$cInterLabs-0015$$x2
001033605 536__ $$0G:(DE-HGF)ZT-I-PF-4-061$$aX-BRAIN (ZT-I-PF-4-061)$$cZT-I-PF-4-061$$x3
001033605 7001_ $$0P:(DE-Juel1)165746$$aDickscheid, Timo$$b1$$ufzj
001033605 7001_ $$0P:(DE-Juel1)192312$$aBenassou, Sabrina$$b2$$ufzj
001033605 7001_ $$0P:(DE-Juel1)206762$$aKasravi, Javad$$b3$$ufzj
001033605 7001_ $$0P:(DE-Juel1)140202$$aStrube, Alexandre$$b4$$ufzj
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001033605 9141_ $$y2024
001033605 920__ $$lyes
001033605 9201_ $$0I:(DE-Juel1)INM-1-20090406$$kINM-1$$lStrukturelle und funktionelle Organisation des Gehirns$$x0
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