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. 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, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i</i> .

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• 1 However, since deep learning methods operate as black boxes, the uncertainty associated with their predictions is often challenging to quantify.
• 2 Bayesian statistics offer a formalism to understand and quantify the uncertainty associated with deep neural network predictions.
• 3 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, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i</i> .

This research advances how AI systems learn, reason, and solve problems — with direct implications for automation and scientific discovery.

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