An artificial neural network is a computational model that approximates a mapping between inputs and outputs.
It is inspired by the structure of the human brain, in that it is similarly composed of a network of interconnected neurons that propagate information upon receiving sets of stimuli from neighbouring neurons.
Training a neural network involves a process that employs the backpropagation and gradient descent algorithms in tandem. As we will be seeing, both of these algorithms make extensive use of calculus.