TY  - GEN
AU  - Strube, Alexandre
AU  - Benassou, Sabrina
AU  - Kasravi, Javad
AU  - Dickscheid, Timo
AU  - Schiffer, Christian
TI  - Deep Learning for Neuroscience
M1  - FZJ-2025-01617
PY  - 2024
AB  - Machine 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.
T2  - Forschungszentrum Jülich
CY  - 19 Nov 2024 - 19 Nov 2024, Jülich (Germany)
Y2  - 19 Nov 2024 - 19 Nov 2024
M2  - Jülich, Germany
LB  - PUB:(DE-HGF)17
UR  - https://juser.fz-juelich.de/record/1038646
ER  -