Understanding advanced regularization techniques

Advanced regularization techniques are methods used in ML and statistical modeling to prevent overfitting and improve the generalization performance of models. Overfitting occurs when a model fits the training data too closely, capturing noise and irrelevant patterns, which leads to poor performance on unseen data. Regularization techniques introduce constraints or penalties to the model’s parameters during training to encourage simpler, more generalized models.

Understanding dropout

Dropout is a regularization technique used in NNs, particularly deep NNs (DNNs), to prevent overfitting. Overfitting occurs when an NN learns to fit the training data too closely, capturing noise and memorizing specific examples rather than generalizing from the data. Dropout is a simple yet effective method for improving a model’s generalization performance.

During the training phase, at each forward and backward pass, dropout randomly...