Reading material for neural networks Class notes: http://hedibert.org/wp-content/uploads/2018/07/dnn.pdf Chapter 4.11 Redes Neurais Artificiais (pages 91-99) of "Aprendizado de Maquina: Uma Abordagem Estatistica" http://www.rizbicki.ufscar.br/AME.pdf Bayesian Neural Nets Lecture notes from Roger Grosse and Jimmy Ba https://www.cs.toronto.edu/~rgrosse/courses/csc421_2019/slides/lec19.pdf Bayesian Methods for Neural Lecture notes from Aaron Courville Networkshttps://www.cs.cmu.edu/afs/cs/academic/class/15782-f06/slides/bayesian.pdf Bayesian Deep Learning meeting http://bayesiandeeplearning.org/ R Packages BRNN: Bayesian Regularization for Feed-Forward Neural Networks Version 0.8 - Date 2020-01-04 Paulino Perez Rodriguez, Daniel Gianola https://cran.r-project.org/web/packages/brnn/brnn.pdf Technical Note: An R package for fitting Bayesian regularized neural networks with applications in animal breeding https://academic.oup.com/jas/article-abstract/91/8/3522/4731320?redirectedFrom=PDF BLNN: An R package for training neural networks using Bayesian inference March 2020 DOI: 10.1016/j.softx.2020.100432 https://www.sciencedirect.com/science/article/pii/S235271101930322X Additional bibliography Bayesian Interpolation David J. C. MacKay (1992) Neural Computation 4, 415-447 https://authors.library.caltech.edu/13792/1/MACnc92a.pdf Radford M. Neal (1996) Bayesian Learning for Neural Networks Book downloadable https://link.springer.com/content/pdf/10.1007%2F978-1-4612-0745-0.pdf Issues in Bayesian Analysis of Neural Network Models Muller and Insua (1998) Neural Computation, Volume 10, Issue 3, pages 749-770 https://doi.org/10.1162/089976698300017737 Hands-on Bayesian Neural Networks - a Tutorial for Deep Learning Users https://arxiv.org/pdf/2007.06823.pdf Fortuin (2021) Priors in Bayesian Deep Learning: A Review https://arxiv.org/abs/2105.06868 Wang and Yeung (2020) A Survey on Bayesian Deep Learning https://dl.acm.org/doi/pdf/10.1145/3409383 The Case for Bayesian Deep Learning Andrew Gordon Wilson (2020) https://arxiv.org/abs/2001.10995 Dropout as a bayesian approximation: Representing model uncertainty in deep learning. Gal and Ghahramani (2016) International conference on machine learning, pages 1050-1059.