Bayesian inference allows us to learn a probability distribution over possible neural networks. We can approximately solve inference with a simple modification to standard neural network tools. The resulting algorithm mitigates overfitting, enables learning from small datasets, and tells us how uncertain our predictions are. What's wrong with neural networks? You may have heard deep neural networks described as powerful function approximators. Their power is due to the extreme flexibility of hav...