05. Juni 2023
08:45
07. Juni 2023
15:00
Englisch
Basis
ThemenbereicheDaten in HPC / Deep Learning / Maschinelles Lernen
ThemenKünstliche Intelligenz
Big Data
Deep Learning
Maschinelles Lernen
Scientific Machine Learning
After this course, participants will
Dr. Khatuna Kakhiani, Patrick Vogler and Dr.-Ing. Lorenzo Zanon (HLRS), and Anna Schwarz (IAG).
(preliminary)
08:45 - 09:00 on every day: drop in to Zoom
Day 1: Focus on Pre-processing, Feature Engineering and Machine Learning (9:00 - 17:00, Dr.-Ing. Lorenzo Zanon)
The first day will be based on the “Stuttgart S-Bahn Example” (originally developed by Dennis Hoppe, HLRS) to provide an introduction to Machine Learning. The focus is on data preparation, classification and regression algorithms in supervised learning: Can these tools be helpful to improve the travel experience in the Stuttgart S-Bahn, which are their limits? Apache Spark will be employed for the hands-on sessions on Jupyter Notebooks as well as via interactive jobs on script. Finally, we will also touch upon the visualisation of results.
Day 2: Focus on data processing, Model of ANN and supervised Deep Learning to classify images of waste in the wild (9:00 - 17:30, Dr. Khatuna Kakhiani)
During this day, participants will explore how Deep Learning can be used to classification waste in wild. After brief introduction of Deep Learning, and basic concepts and Building blocks of Deep Neural Networks, participants will learn how to:
Day 3:
On the third day we start with the guest lecture "Towards Data-Driven Computational Fluid Dynamics". It will be given by Anna Schwarz, Institute of Aerodynamics and Gas Dynamics, University of Stuttgart.
We will conclude the half-day with an introduction to data compression, focusing on the various methods available to us for the efficient size reduction of our training data. Special attention will be paid to which approaches are best suited for different data types and what impact the different approaches and compression rates have on the quality of the datasets. The compression library BigWhoop and its accompanying command line tool will be made available for the hands-on sessions.
Lunch break will be from 13:00-14:00 on the first two days.
The exercises will be carried out on HLRS's systems using Jupyter Notebooks.
Register via the button at the top of this page.
We encourage you to register to the waiting list if the course is full. Places might become available.
Registration closes on May 22, 2023.
Late registrations after that date might still be possible according to the course capacity.
Students without Diploma/Master: 30 EUR
PhD students or employees at a German university or public research institute: 60 EUR
PhD students or employees at a university or public research institute in an EU, EU-associated or PRACE country other than Germany: 120 EUR.
PhD students or employees at a university or public research institute outside of EU, EU-associated or PRACE countries: 240 EUR
Other participants, e.g., from industry, other public service providers, or government: 600 EUR
Our course fee includes coffee breaks (in classroom courses only).
For lists of EU and EU-associated coutries, and PRACE countries have a look at the Horizon Europe and PRACE website.
HLRS is part of the Gauss Centre for Supercomputing (GCS), together with JSC in Jülich and LRZ in Garching near Munich. EuroCC@GCS is the German National Competence Centre (NCC) for High-Performance Computing. HLRS is also a member of the Baden-Württemberg initiative bwHPC.
This course is provided within the framework of the bwHPC training program.
This course is partly realised in cooperation with the Centre of Excellence EXCELLERAT P2 (funded by the European Union, grant agreement No 101092621). See also the EXCELLERAT Service Portal for more information.
Tobias Haas, phone 0711 685 87223, tobias.haas(at)hlrs.de
See the training overview and the Supercomputing Academy pages.
Mai 05 - 08, 2025
Online
Mai 09 - 23, 2025
Hybrid Event - Stuttgart, Germany
Mai 27 - 28, 2025
Online
Juni 03 - 24, 2025
Online (flexible)
Juli 09 - 10, 2025
Online