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Trainingskurs "Deep Learning using a Convolutional Neural Network"

(Kurs-Nr. 2292018 im Trainingsprogramm 2018 des Forschungszentrums)

06.06.2018 09:00 Uhr
07.06.2018 16:30 Uhr
Jülich Supercomputing Centre, Rotunde, Geb. 16.4, Raum 301


Mitarbeiter, die Deep-Learning-Methoden kennen lernen wollen.

This course focuses on a recent machine learning method known as deep learning that emerged as a promising disruptive approach, allowing knowledge discovery from large datasets in an unprecedented effectiveness and efficiency. It is particularly relevant in research areas, which are not accessible through modelling and simulation often performed in HPC. Traditional learning, which was introduced in the 1950s and became a data-driven paradigm in the 90s, is usually based on an iterative process of feature engineering, learning, and modelling. Although successful on many tasks, the resulting models are often hard to transfer to other datasets and research areas.

This course provides an introduction into deep learning and its inherent ability to derive optimal and often quite generic problem representations from the data (aka ‘feature learning’). Concrete architectures such as Convolutional Neural Networks (CNNs) will be applied to real datasets of applications using known deep learning frameworks such as Tensorflow, Keras, or Torch. As the learning process with CNNs is extremely computational-intensive the course will cover aspects of how parallel computing can be leveraged in order to speed-up the learning process using general purpose computing on graphics processing units (GPGPUs). Hands-on exercises allow the participants to immediately turn the newly acquired skills into practice. After this course participants will have a general understanding for which problems CNN learning architectures are useful and how parallel and scalable computing is facilitating the learning process when facing big datasets.

Participants should be able to work on the Unix/Linux command line, have a basic level of understanding of batch scripts required for HPC application submissions, and have a minimal knowledge of probability, statistics, and linear algebra.
Der Kurs wird auf Englisch gehalten.
2 Tage
6. - 7. Juni 2018, 9.00 - 16.30 Uhr
Jülich Supercomputing Centre, Rotunde, Geb. 16.4, Raum 301
maximal 40, die in 2er-Gruppen zusammenarbeiten
Prof. Dr. Morris Riedel, JSC
Photo Dr. Gabriele Cavallaro
Dr. Gabriele Cavallaro
Telefon: +49 2461 61-3858
Bitte schicken Sie Ihre Anmeldung an Gabriele Cavallaro bis 25. Mai 2018.
Wenn Sie nicht Mitarbeiter des Forschungszentrums Jülich sind, geben Sie bei der Anmeldung bitte die folgenden Daten an:
Vorname, Name, Geburtsdatum, Nationalität, vollständige Adresse des Wohnorts, E-Mail-Adresse