When training a deep learning model, you observe that the accuracy of the model decreases after a few epochs. How will you address this problem?
A decrease in the accuracy of a deep learning model after some epochs means that the model learns from the dataset's properties and does not consider the features. This is known as overfitting a deep learning model. Either dropout regularization or early termination can be used to solve this problem. As the formula suggests, stopping early stops further training of the deep learning model when we notice that the model's inaccuracy has decreased. Dropout regularization is dropping some nodes or output layers so that the remaining nodes have different weights.