The lecture covers deep learning concepts and techniques, including:
- general ideas and mathematical background
- training and regularization methods
- neural network architectures (feed-forward, convolutional, physics-informed, autoenconder,...)
- application to scientific and engineering problems
- employment on modern computer hardware
In hands-on sessions, practical exercises are used to discuss and illustrate the presented content.
- Kursleiter/in: Andreas Vogel
Semester: ST 2025