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</p> Signup and view all the answers

    What is regularization intended to prevent?

    <p>Overfitting</p> Signup and view all the answers

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

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

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

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

    What is the goal of optimizing the loss function?

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

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