To actually implement a multilayer perceptron learning algorithm, we do not want to hard code the update rules for each weight. Instead, we can formulate both feedforward propagation and backpropagation as a series of matrix multiplies, which leads to better usability. This is what leads to the impressive performance of neural nets - dumping matrix multiplies to a graphics card allows for massive parallelization and large amounts of data.

This tutorial will cover how to build a neural network that uses matrices. It builds heavily off of the theory in Part 4 of this series, so make sure you understand the math there!