Deep Learning, Regularisation, Optimization Algorithms, Deep Neural Networks, Gradient Descent.
model (i.e., the model that has performed well on both training as well as dev set), we evaluate it on the test set in order to get an unbiased estimate of how well our algorithm is doing. There is still one question left after this — what should be the length of these training, dev and test sets?