Deep learning belongs to the class of machine learning methods and typically employs neural networks with many layers to solve tasks such as classification or function approximation. Due to their flexibility neural networks are widely applicable and have shown extraordinary performance, for example in autonomous driving, computer-aided diagnosis, or the automatic segmentation of images. To make deep learning applicable for metrology, it is crucial to understand and evaluate the reliability of these methods. One important issue in this regard is to quantify the uncertainties associated with their results. Models employed in metrology are usually well understood and often based on physical knowledge. Deep learning, on the other hand, builds its models directly from data. Another challenge in the application of deep learning for metrology therefore is to understand the behavior of the empirically built models.