Validation, Overfitting, Underfitting, Overfitting Underfitting, Machine Learning.
the parameters that will achieve the minimum loss instead we stop the model training at the point where our validation loss is minimized. This is a very common method to control the over-fitting in the neural networks. 2. Dropout — Dropout as the name suggests is a technique in which we drop out