Description: Gradient penalty is a regularization technique used in the field of machine learning and model optimization. Its main goal is to prevent overfitting, which occurs when a model fits too closely to the training data, losing its ability to generalize to new data. This technique adds a penalty term to the loss function, based on the gradients of the model parameters. By doing so, it aims to restrict the magnitude of the gradients, promoting simpler and more robust solutions. There are different forms of penalty, with the most common being L1 (Lasso) and L2 (Ridge) regularization. L1 penalty tends to produce sparser models by eliminating some features, while L2 distributes the penalty more evenly across all parameters. Gradient penalty is particularly relevant in complex models, where the number of parameters can be high compared to the amount of available data. By incorporating this technique, models not only become more interpretable but also improve their performance on unseen datasets, which is crucial in practical applications such as sales forecasting, image recognition, and natural language processing.
