Qualification objectives: The students have a basic understanding of algebraic and computational methods for deep neural networks, their application scope and can practically build and train them with state-of-the-art software tools. They are familiar with typical deep learning structures and understand the relationship to their shallow counterparts.
- Multilayer neural network and universal represenation theorem
- Deep feedforward networks
- Convolutional Neural Networks
- Autoencoder versus principal component analysis
- Time-autoencoder versus time-lagged independent component analysis
- Generative networks: Variational Autoencoders and Adversarial Generative Networks
- Active learning
This lecture/lab course is suitable for Master students of Mathematics, Computer Science or Computational Sciences.
wöchentlich, ab 17.04.2020, 14:00 - 16:00 (11 Termine)