Last time, we explored how a simple MLP neural network could be used to classify the MNIST dataset. Today, we will work on a messier problem. We will use a modified version of the Stanford dogs dataset to train a neural network that can classify dog breeds. Since inter-class variations are small, and an obscure detail could be the deciding factor, we will need a model that can capture more detail. This is where convolutional neural networks (CNN) come in.
As always, we will start by explaining some of the high-level concepts. You can follow along with the code here.