Symbol Kurs

2400179 – Geometric Deep Learning

This module provides students with both theoretical and practical insights into modern Deep Learning. In particular, we focus on a novel approach for understanding deep neural networks with mathematical tools from geometry and group theory. This enables a methodical approach to Deep Learning: starting from first principles of symmetry and invariance, we derive different network architectures for analyzing unstructured sets, grids, graphs, and manifolds. Topics of the course include: group theory, graph neural networks, convolutional neural networks, applications of geometric deep learning in diverse fields such as geometry processing, molecular dynamics, social networks, game playing (computer Go), processing of text and speech, as well as applications in medicine.

Zusammenfassung

This module provides students with both theoretical and practical insights into modern Deep Learning.
In particular, we focus on a novel approach for understanding deep neural networks with mathematical tools from geometry and group theory.
This enables a methodical approach to Deep Learning: starting from first principles of symmetry and invariance, we derive different network architectures for analyzing unstructured sets, grids, graphs, and manifolds.
Topics of the course include: group theory, graph neural networks, convolutional neural networks, applications of geometric deep learning in diverse fields such as geometry processing, molecular dynamics, social networks, game playing (computer Go), processing of text and speech, as well as applications in medicine.

Allgemein

Sprache
Deutsch

Verfügbarkeit

Zugriff
19. Okt 2022, 16:40 - 31. Mär 2023, 16:45
Aufnahmeverfahren
Sie können diesem Kurs direkt beitreten.
Zeitraum für Beitritte
Unbegrenzt
Freie Plätze
33
Veranstaltungszeitraum
26. Okt 2022 - 15. Feb 2023

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Zusätzliche Informationen

Objekt-ID
2603534
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Erstellt am
19. Okt 2022, 16:36