Taking a small break from some of the heavier math, I thought I'd write a post (aka learn more about) a very popular neural network architecture called Residual Networks aka ResNet. This architecture is being very widely used because it's so simple yet so powerful at the same time. The architecture's performance is due its ability to add hundreds of layers (talk about deep learning!) without degrading performance or adding difficulty to training. I really like these types of robust advances where it doesn't require fiddling with all sorts of hyper-parameters to make it work. Anyways, I'll introduce the idea and show an implementation of ResNet on a few runs of a variational autoencoder that I put together on the CIFAR10 dataset.