The document discusses Bayesian neural networks and related topics. It covers Bayesian neural networks, stochastic neural networks, variational autoencoders, and modeling prediction uncertainty in neural networks. Key points include using Bayesian techniques like MCMC and variational inference to place distributions over the weights of neural networks, modeling both model parameters and prediction
![(DL hacks輪読)Bayesian Neural Network](https://cdn-ak-scissors.b.st-hatena.com/image/square/989de6bd0d82b78b37b67b39399cade36de5da54/height=288;version=1;width=512/https%3A%2F%2Fcdn.slidesharecdn.com%2Fss_thumbnails%2F20170127-170130120133-thumbnail.jpg%3Fwidth%3D640%26height%3D640%26fit%3Dbounds)