Early stopping is the termination of the training process based on a model's performance on the validation dataset. This is the sole purpose of the validation dataset in our study. A patience of 500 epochs was used in all cases, meaning that model training stopped once the validation loss had not improved for that long. The best version of the model parameters, based on the validation loss, was then restored. An upper limit of 8000 epochs was set, to avoid perpetual training due to marginal or random improvements of the validation loss. Dropout is defined as the random and temporary 'switching off' of neural network nodes at the start of each training epoch. The effect of dropout is to make each hidden node more robust and drive it towards creating useful features on its own, without relying on other nodes to correct its mistakes . Essential concepts such as model capacity and regularisation in artificial neural networks are thoroughly explained in the textbook of Goodfellow, Bengio and Courville .