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Samples and documentation for the "Getting started with Deep Learning on Supercomputers" tutorial.
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Simulation module
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The tutorial has been moved to: https://gitlab.version.fz-juelich.de/hpc4ns/dl_on_supercomputers
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a tool to enable fast and flexible Machine Learning on Air data time series
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Reporting module
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Visualization module
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Framework for simulating pedestrians with state of the art models.
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ParaViewWeb RemoteRenderer in Jupyter
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Sparse Matrix Generator with Given Spectrum
Homepage: https://smg2s.github.io
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Run Zhang Master Thesis. Title: "Super-Resolution of Sentinel-2 Images with Generative Adversarial Networks". 2020
Enhancing Resolution of Sentinel 2 Hyperspectral Satellite Images with Super-resolution Generative Adversarial Networks
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A library of Python utilities developed at the Simulation and Data Laboratory Neuroscience, and the High Performance Computing in Neuroscience (HPCNS) division of the Jülich Supercomputing Center.
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Here is the repository of the on-going project to develop a stand-alone sager with Python to using for I/O, Pre/Post-Processing
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Wiki for material and resources, Deep Learning for COVID XRay detection