DLAV Lecture 3: Data Loss and Regularization

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Questions and Answers

In order to reduce generalization error, which of the following is an important consideration?

  • Increasing the number of layers
  • Simply using more training data
  • Using a different architecture
  • Selecting the right hyper-parameters (correct)

What is the consequence of having a loss function of zero?

  • The model is unique
  • The model is not unique (correct)
  • The model is overfitting
  • The model is underfitting

Why might increasing the magnitude of the weights not improve the model?

  • It can lead to overfitting (correct)
  • It does not affect the model's performance
  • It is not possible to increase the magnitude of the weights
  • It can lead to underfitting

What is the goal of training a model?

<p>To minimize the loss function (D)</p> Signup and view all the answers

What is regularization intended to prevent?

<p>Overfitting (C)</p> Signup and view all the answers

What is the effect of doubling the weights in the model?

<p>The loss function remains unchanged (D)</p> Signup and view all the answers

What is the purpose of the loss function in training a model?

<p>To evaluate the model's performance (A)</p> Signup and view all the answers

Why is it important to match model predictions with training data?

<p>To improve model performance (C)</p> Signup and view all the answers

What is the relationship between the loss function and the model's performance?

<p>A lower loss function indicates good performance (B)</p> Signup and view all the answers

What is the goal of optimizing the loss function?

<p>To minimize the loss function (B)</p> Signup and view all the answers

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