What is Weight Decay

Weight Decay is a regularization technique widely used in machine learning and deep learning aimed at preventing overfitting.

It works by adding a penalty term to the loss function that discourages large weight values, effectively encouraging the model to learn smaller weights.

This technique is particularly beneficial for complex models and high-dimensional datasets, as it helps the model to generalize better when encountering unseen data.

Weight Decay is often used in conjunction with other regularization methods, such as Dropout, to enhance the robustness of the model.

As deep learning technology advances, Weight Decay may evolve further through adaptive learning rates and more sophisticated optimization techniques.