The project focuses on understanding and analyzing the Dropout technique in deep learning.

It explores how Dropout prevents overfitting and enhances generalization in neural networks through both theoretical and practical perspectives.

The project highlights the mathematical foundation behind Dropout and its role as a powerful regularization method in modern AI.