Understanding Neural Networks -- Part 1/3: Intuition of Forward Propagation

Basically, it's just a type of ML algorithm that was built to emulate connections in a brain. It can be used for classification and regression tasks. Today, we're going to go over a classification task. The big thing about NNs is that they are "universal function approximators," meaning they can approximate any function (duh). Compare this with linear regression which ONLY can approximate linear functions. The first layer is called the input layer and has as many neurons as we have features in our data.