The course aims to teach you the basic knowledge of hyperparamter optimization such that an appropriate set of numerical parameters for a learning algorithm can be found. The acquired knowledge is deepened in a two-hour practical session using Jupyter Notebooks.
Part I: Theory
Part II: Practical exercises "Hyperparameter optimization for the improvement of neural networks"
Dr Alexander Rüttgers and Dr Charlotte Debus, DLR (Cologne)
The course is designed for 50 students. There are no course fees, but you will have to cover the travel expenses yourself.
Please register at firstname.lastname@example.org by October 16, 2020.