Podcast
Questions and Answers
What is the main goal of regularization in deep learning models?
What is the main goal of regularization in deep learning models?
- To increase model complexity
- To decrease model performance
- To reduce training error
- To reduce generalization error (correct)
What happens when the regularization parameter alpha is set to 0?
What happens when the regularization parameter alpha is set to 0?
- The model will underfit
- The regularization penalty will be infinite
- The model will overfit (correct)
- The model will perform optimally
What is the difference between L1 and L2 parameter regularization?
What is the difference between L1 and L2 parameter regularization?
- L1 is also known as Tikhonov regularization, while L2 is known as weight decay
- L1 penalizes the weights only, while L2 penalizes both weights and bias
- L1 results in sparse models, while L2 does not (correct)
- L1 penalizes the bias term, while L2 penalizes the weights only
What is the update rule for L2 parameter regularization?
What is the update rule for L2 parameter regularization?
What is the purpose of early stopping?
What is the purpose of early stopping?
What is the definition of regularization in deep learning?
What is the definition of regularization in deep learning?
What is Model Occam’s Razor and how is it related to deep learning?
What is Model Occam’s Razor and how is it related to deep learning?
What is L2 parameter regularization and how does it work?
What is L2 parameter regularization and how does it work?
What is the difference between underfitting and overfitting in deep learning?
What is the difference between underfitting and overfitting in deep learning?
What is early stopping and how does it prevent overfitting in deep learning?
What is early stopping and how does it prevent overfitting in deep learning?