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| 001 | 1038646 | ||
| 005 | 20250203215421.0 | ||
| 037 | _ | _ | |a FZJ-2025-01617 |
| 041 | _ | _ | |a English |
| 100 | 1 | _ | |a Strube, Alexandre |0 P:(DE-Juel1)140202 |b 0 |u fzj |
| 111 | 2 | _ | |a Forschungszentrum Jülich |g INM Retreat |c Jülich |d 2024-11-19 - 2024-11-19 |w Germany |
| 245 | _ | _ | |a Deep Learning for Neuroscience |
| 260 | _ | _ | |c 2024 |
| 336 | 7 | _ | |a lecture |2 DRIVER |
| 336 | 7 | _ | |a Generic |0 31 |2 EndNote |
| 336 | 7 | _ | |a MISC |2 BibTeX |
| 336 | 7 | _ | |a Lecture |b lecture |m lecture |0 PUB:(DE-HGF)17 |s 1738563473_21854 |2 PUB:(DE-HGF) |x Other |
| 336 | 7 | _ | |a LECTURE_SPEECH |2 ORCID |
| 336 | 7 | _ | |a Text |2 DataCite |
| 520 | _ | _ | |a 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. |
| 536 | _ | _ | |a 5112 - Cross-Domain Algorithms, Tools, Methods Labs (ATMLs) and Research Groups (POF4-511) |0 G:(DE-HGF)POF4-5112 |c POF4-511 |f POF IV |x 0 |
| 536 | _ | _ | |a 5251 - Multilevel Brain Organization and Variability (POF4-525) |0 G:(DE-HGF)POF4-5251 |c POF4-525 |f POF IV |x 1 |
| 536 | _ | _ | |a Helmholtz AI Consultant Team FB Information (E54.303.11) |0 G:(DE-Juel-1)E54.303.11 |c E54.303.11 |x 2 |
| 700 | 1 | _ | |a Benassou, Sabrina |0 P:(DE-Juel1)192312 |b 1 |u fzj |
| 700 | 1 | _ | |a Kasravi, Javad |0 P:(DE-Juel1)206762 |b 2 |u fzj |
| 700 | 1 | _ | |a Dickscheid, Timo |0 P:(DE-Juel1)165746 |b 3 |u fzj |
| 700 | 1 | _ | |a Schiffer, Christian |0 P:(DE-Juel1)170068 |b 4 |u fzj |
| 856 | 4 | _ | |u https://events.hifis.net/event/1825/page/483-tutorials-november-19-2024 |
| 909 | C | O | |o oai:juser.fz-juelich.de:1038646 |p VDB |
| 910 | 1 | _ | |a Forschungszentrum Jülich |0 I:(DE-588b)5008462-8 |k FZJ |b 0 |6 P:(DE-Juel1)140202 |
| 910 | 1 | _ | |a Forschungszentrum Jülich |0 I:(DE-588b)5008462-8 |k FZJ |b 1 |6 P:(DE-Juel1)192312 |
| 910 | 1 | _ | |a Forschungszentrum Jülich |0 I:(DE-588b)5008462-8 |k FZJ |b 2 |6 P:(DE-Juel1)206762 |
| 910 | 1 | _ | |a Forschungszentrum Jülich |0 I:(DE-588b)5008462-8 |k FZJ |b 3 |6 P:(DE-Juel1)165746 |
| 910 | 1 | _ | |a Forschungszentrum Jülich |0 I:(DE-588b)5008462-8 |k FZJ |b 4 |6 P:(DE-Juel1)170068 |
| 913 | 1 | _ | |a DE-HGF |b Key Technologies |l Engineering Digital Futures – Supercomputing, Data Management and Information Security for Knowledge and Action |1 G:(DE-HGF)POF4-510 |0 G:(DE-HGF)POF4-511 |3 G:(DE-HGF)POF4 |2 G:(DE-HGF)POF4-500 |4 G:(DE-HGF)POF |v Enabling Computational- & Data-Intensive Science and Engineering |9 G:(DE-HGF)POF4-5112 |x 0 |
| 913 | 1 | _ | |a DE-HGF |b Key Technologies |l Natural, Artificial and Cognitive Information Processing |1 G:(DE-HGF)POF4-520 |0 G:(DE-HGF)POF4-525 |3 G:(DE-HGF)POF4 |2 G:(DE-HGF)POF4-500 |4 G:(DE-HGF)POF |v Decoding Brain Organization and Dysfunction |9 G:(DE-HGF)POF4-5251 |x 1 |
| 914 | 1 | _ | |y 2024 |
| 920 | _ | _ | |l yes |
| 920 | 1 | _ | |0 I:(DE-Juel1)JSC-20090406 |k JSC |l Jülich Supercomputing Center |x 0 |
| 920 | 1 | _ | |0 I:(DE-Juel1)INM-1-20090406 |k INM-1 |l Strukturelle und funktionelle Organisation des Gehirns |x 1 |
| 980 | _ | _ | |a lecture |
| 980 | _ | _ | |a VDB |
| 980 | _ | _ | |a I:(DE-Juel1)JSC-20090406 |
| 980 | _ | _ | |a I:(DE-Juel1)INM-1-20090406 |
| 980 | _ | _ | |a UNRESTRICTED |
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