Hands-On Bayesian Neural Networks—A Tutorial for Deep Learning Users

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 deep learning practitioners with 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.

• DA
  Defense Advanced Research Projects Agency

  Awards: FA8750-19-2-0501, FA8750
• AR
  Australian Research Council

  Awards: DP150104251, DP150100294, DP210101682
• AF
  Air Force Research Laboratory

  Award: FA8750-19-2-0501