Summary
In this chapter we discussed the math behind deep learning. Put simply, a deep learning model computes a function given an input vector to produce the output. The interesting part is that we can literally have billions of parameters (weights) to be tuned. Backpropagation is a core mathematical algorithm used by deep learning for efficiently training artificial neural networks following a gradient descent approach that exploits the chain rule. The algorithm is based on two steps repeated alternatively: the forward step and the backstep.
During the forward step inputs are propagated through the network in order to predict outputs. These predictions might be different from the true values given to assess the quality of the network. In other words, there is an error and our goal is to minimize it. This is where the backstep plays a role, by adjusting the weights of the network to minimize the error.
The error is computed via loss functions such as MSE, or cross-entropy for...
