  Modern deep learning methods constitute incredibly powerful tools to tackle a
myriad of challenging problems. However, since deep learning methods operate as
black boxes, the uncertainty associated with their predictions is often
challenging to quantify. Bayesian statistics offer a formalism to understand
and quantify the uncertainty associated with deep neural network predictions.
This tutorial provides an overview of the relevant literature and a complete
toolset to design, implement, train, use and evaluate Bayesian Neural Networks,
i.e. Stochastic Artificial Neural Networks trained using Bayesian methods.
