This article takes a look at principal component analysis (PCA). In the course of the article, weMEHR ERFAHREN
„Are you sure about that?! Uncertainty Quantification in AI“
With the advent of Deep Learning (DL), the field of AI made a giant leap forward and it is nowadays applied in many industrial use-cases. Especially critical systems like autonomous driving, require that DL methods not only produce a prediction but also state the certainty about the prediction in order to assess risks and failure.
In my talk, I will give an introduction to different kinds of uncertainty, i.e. epistemic and aleatoric. To have a baseline for comparison, the classical method of Gaussian Processes for regression problems is presented. I then elaborate on different DL methods for uncertainty quantification like Quantile Regression, Monte-Carlo Dropout, and Deep Ensembles. The talk is concluded with a comparison of these techniques to Gaussian Processes and the current state of the art.
Event: PyCon/PyData Berlin 2019
This article sheds light on the question of why machine learning products mostly do not get intoMEHR ERFAHREN