Combating Overfitting With Dropout Regularization

Dropout regularization is a machine learning technique that is used to combat overfitting. It works by randomly removing neurons from the model, allowing it to focus on the general features of the data rather than the fine details. This article explores how dropout regularization works, how to implement it, and the benefits and disadvantages of this technique compared to other methods.