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 is the primary reason for penalizing large weights in a model?

• To increase the model's complexity

• To prevent overfitting

• To enhance the model's accuracy

• To reduce the model's training time

Which regularization method involves the sum of absolute values of weights?

• Dropout Regularization

• Batch Normalization

• L1 Regularization

• L2 Regularization

What is the role of the hyperparameter Lambda in regularization?

• To increase the learning rate

• To adjust the impact of regularization

• To set the initial weights of the model

• To determine the number of layers in a model

Which regularization technique is more suitable for feature selection?

• Dropout

• Batch Normalization

• L1 Regularization

• L2 Regularization

Why is L2 regularization preferred for training models?

• It requires no hyperparameter tuning

• It offers continuous sparsity

• It is computationally less expensive

• It provides discrete sparsity