A single layered network

Right, now we have seen how to leverage two versions of our perception unit, in parallel, enabling each individual unit to learn a different underlying pattern that is possibly present in the data we feed it. We naturally want to connect these neurons to output neurons, which fire to indicate the presence of a specific output class. In our sunny-rainy day classification example, we have two output classes (sunny or rainy), hence a predictive network tasked to solve this problem will have two output neurons. These neurons will be supported by the learning of neurons from the previous layer, and ideally will represent features that are informative for predicting either a rainy or a sunny day. Mathematically speaking, all that is simply happening here is the forward propagation of our transformed input features, followed by the backward propagation of the...