Model training is the process by which neural networks are fit to a set of data. During training, data is input into the network and an output is generated via forward propagation. A loss function is calculated using the expected and actual output. The gradient of the loss function is computed using backpropagation, which is then used to optimise the weights in order to minimise the loss via gradient descent.