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

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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.
Original languageEnglish
Pages (from-to)29-48
JournalIEEE Computational Intelligence Magazine
Issue number2
Publication statusPublished - May 2022


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