Deep Learning, Regularisation, Optimization Algorithms, Deep Neural Networks, Gradient Descent.
it can find, the model tends to perform extremely well on the training set but fails to produce good results on the dev and test sets. It falls apart when faced with previously unseen data. One way to prevent overfitting is to reduce the complexity of the model. This is exactly what regularization