A model has many weights, and some are very large, making it too complex. What technique adds a penalty to make these weights smaller during training?

The technique that adds a penalty to make weights smaller during training to reduce model complexity is called Regularization. When a model has many very large weights, it can become overly sensitive to small fluctuations in the training data, leading to overfitting. Overfitting means the model performs exceptionally well on the training data but poorly on new, unseen data because it has learned the noise or specific patterns of the training set too closely rather than generalizing. Regularization addresses this by adding a penalty term to the model's loss function. The loss function quantifies how well the model is performing, with lower values indicating better performance. During training, the model tries to minimize this loss. By adding a penalty for large weights to the loss fun....