From A guide to an efficient way to build neural network architectures- Part I: Hyper-parameter… by Shashank Ramesh

Train set is the set of data-points on which the model learns/trains, Validation set is used to compare between models and Test set to see how well does our model generalize to unseen input (input which we have not trained upon)

From A guide to an efficient way to build neural network architectures- Part I: Hyper-parameter… by Shashank Ramesh

Hyper-parameter is a configurable value which is set before the learning process begins. These hyper-parameter values dictate the behavior of the training algorithm and how it learns the parameters from the data.