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Keywords: Graph Neural Networks  or Autoencoder  or Normalizing Flows  or Accelerator Optimised Progr... 

  • Deep Learning School

    21 - 24 May 2024

    Filderstadt, Germany

    Deep Learning School The Active Training Course "Advanced Deep Learning" is hosted over 4 days at (location tba) by the community organization DIG-UM with support from the BMBF-funded ErUM-Data-Hub. The event serves the professional education of young scientists belonging to the ErUM Community. The intensive course on Graph Neural Networks, Transformers, Normalizing Flows and Autoencoders will be held from 21.05.24 - 24.05.24. The course includes a challenge to be worked out and presented by participant subgroups. The workshop is aimed at deep-learning enthusiasts from all ErUM communities (Research on Universe and Matter) who have prior knowledge of neural networks and applied basic concepts of deep learning. A fee of 300€ will be charged for participation in the course. The workshop fee includes the cost of the workshop, accommodation and catering. Registration closes tba. 2024-05-21 17:00:00 UTC 2024-05-24 14:00:00 UTC ErUM-Data-Hub (BMBF) Bernhäuser Forst, Filderstadt, Germany Bernhäuser Forst Filderstadt Germany 70794 [] [] [] workshops_and_courses [] Graph Neural NetworksAutoencoderModel DiffusionNormalizing Flows
  • Fast and Efficient Python Computing School

    24 - 27 September 2024

    Fast and Efficient Python Computing School This is a School organized by the ErUM-Data-Hub with support from DIG-UM. In this school you will learn how Python code can be accelerated. A focus will be placed on numeric NumPy-like array computations. In addition, running these array computations on hardware accelerators, i.e., GPUs, will play a key role in this school. The school is intended for young researchers - especially for PhD students - who regularly work with the scientific Python ecosystem. Requirements are good knowledge of the scientific Python ecosystem, basics of the C++ programming language are beneficial. The school (see timetable) is split into five parts of which three are keynote lectures with hands-on tutorials. The other two comprise an opening talk and a coding group challenge for the participants. A fee of 300€ will be charged for participation in the workshop. ## Topics: * Setting the scene: benefits and disadvantages of the Python programming language and a brief outline how Python programs can be accelerated in general. * Efficient Python Programming: general approaches to accelerate Python code, using C++ in Python and accelerating array computations. * Accelerator Optimised Programming: how array computations can be run on GPUs with typical Deep Learning libraries, such as JAX or TensorFlow. * GPU Programming: understanding the hardware layout, i.e., thread and memory layout and hierarchy and basics of the CUDA toolkit. A focus is set on how to program custom GPU kernels for, e.g., Deep Learning applications (in JAX or TensorFlow). * Group Challenge 2024-09-24 09:30:00 UTC 2024-09-27 18:00:00 UTC [] [] [] workshops_and_courses [] PythonGPUAccelerator Optimised Programming

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