Deep Learning

The course is about neural networks and gives an introduction to the field of deep learning. The content includes the components used to construct deep neural networks, e.g., activation functions, loss functions, regularization techniques (e.g., normalization and dropout), optimization methods (specifically variants of stochastic gradient descent), network architectures. Also covered is deep generative models. The students learn to apply their knowledge by implementing and training modern network architectures and deep learning methods on large data sets. The course is split into two modules: Theory, 5.5 credits Laboration, 2.0 credits