Deep Learning Model Strategies and Techniques Quiz

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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?

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?

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?

w ← w - 𝜖(α𝒘 + ∇𝒘 𝐽(𝒘; 𝑿, 𝒚)) Signup and view all the answers

What is the purpose of early stopping?

To prevent overfitting Signup and view all the answers

What is the definition of regularization in deep learning?

Regularization is any modification made to a learning algorithm that is intended to reduce its generalization error but not its training error. Signup and view all the answers

What is Model Occam’s Razor and how is it related to deep learning?

Model Occam's Razor is the principle that a model should be 'simple', meaning the simplest explanation is usually the correct one. It is related to deep learning because deep learning models can be regularized with the goal of reducing their generalization error while keeping the model simple. Signup and view all the answers

What is L2 parameter regularization and how does it work?

L2 parameter regularization is a strategy to regularize deep learning models by adding a penalty term to the objective function. It penalizes the weights of the model, making them smaller and reducing the risk of overfitting. The penalty term is typically set as the L2 norm of the weight vector. Signup and view all the answers

What is the difference between underfitting and overfitting in deep learning?

Underfitting occurs when a model is too simple to capture the complexity of the data, resulting in high training and test errors. Overfitting occurs when a model is too complex and fits the training data too closely, resulting in low training error but high test error. Signup and view all the answers

What is early stopping and how does it prevent overfitting in deep learning?

Early stopping is a regularization technique that stops training a model when the performance on a validation set stops improving. It prevents overfitting by stopping the training before the model starts fitting the noise in the training data. Signup and view all the answers