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.

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动手操作贝叶斯神经网络——深度学习用户教程

现代深度学习方法构成了非常强大的工具，可以解决无数具有挑战性的问题。然而，由于深度学习方法作为黑匣子运行，与其预测相关的不确定性通常难以量化。贝叶斯统计提供了一种形式主义来理解和量化与深度神经网络预测相关的不确定性。本教程为深度学习从业者提供了相关文献的概述以及用于设计、实施、训练、使用和评估贝叶斯神经网络的完整工具集，一世 。 例如，使用贝叶斯方法训练的随机人工神经网络。