kyle-dorman/bayesian-neural-network-blogpost

In this blog post, I am going to teach you how to train a Bayesian deep learning classifier using Keras and tensorflow. I will then cover two techniques for including uncertainty in a deep learning model and will go over a specific example using Keras to train fully connected layers over a frozen ResNet50 encoder on the cifar10 dataset. With this example, I will also discuss methods of exploring the uncertainty predictions of a Bayesian deep learning classifier and provide suggestions for improving the model in the future. This post is based on material from two blog posts (here and here) and a white paper on Bayesian deep learning from the University of Cambridge machine learning group. If you want to learn more about Bayesian deep learning after reading this post, I encourage you to check out all three of these resources. Thank you to the University of Cambridge machine learning group for your amazing blog posts and papers. Bayesian statistics is a theory in the field of statistics in which the evidence about the true state of the world is expressed in terms of degrees of belief. The combination of Bayesian statistics and deep learning in practice means including uncertainty in your deep learning model predictions. The idea of including uncertainty in neural networks was proposed as early as 1991.