Deep Learning - Deep Neural Network for Beginners Using Python - L1 and L2 Regularization

The video tutorial discusses the issue of overfitting in models due to large coefficients and introduces regularization as a solution. It explains two methods of regularization: L1 and L2. L1 regularization is useful for feature selection due to its sparsity, while L2 is better for training models as it provides continuous sparsity. The tutorial also highlights the importance of fine-tuning the hyperparameter Lambda, which is used in both methods to penalize large weights. The video concludes by discussing scenarios where each regularization method is applicable.

What are the two methods mentioned for regularization?

Why is L2 regularization preferred for training models?