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A Comprehensive Introduction to Bayesian Deep Learning - Joris Baan

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Bayesian (deep) learning has always intrigued and intimidated me. Perhaps because it leans heavily on probabilistic theory, which can be daunting. I noticed that even though I knew basic probability theory, I had a hard time understanding and connecting that to modern Bayesian deep learning research. The aim of this blogpost is to bridge that gap and provide a comprehensive introduction. Instead of starting with the basics, I will start with an incredible NeurIPS 2020 paper on Bayesian deep learning and generalization by Andrew Wilson and Pavel Izmailov (NYU) called Bayesian Deep Learning and a Probabilistic Perspective of Generalization. This paper serves as a tangible starting point in which we naturally encounter Bayesian concepts in the wild. I hope this makes the Bayesian perspective more concrete and speaks to its relevance. I will start with the paper abstract and introduction to set the stage. As we encounter Bayesian concepts, I will zoom out to give a comprehensive overview w

A Comprehensive Introduction to Bayesian Deep Learning - Joris Baan A Comprehensive Introduction to Bayesian Deep Learning | Joris Baan Originally posted on TowardsDataScience . Table of Contents Preamble Neural Network Generalization Back to Basics: The Bayesian Approach Frequentists Bayesianists Bayesian Inference and Marginalization How to Use a Posterior in Practice? Maximum A Posteriori Estimation Full Predictive Distribution Approximate Predictive Distribution Bayesian Deep Learning Recent Approaches to Bayesian Deep Learning Back to the Paper Deep Ensembles are BMA Combining Deep Ensemb

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