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Week 1 Quiz - Practical aspects of deep learning

  1. If you have 10,000,000 examples, how would you split the train/dev/test set?

    • 98% train . 1% dev . 1% test
  2. The dev and test set should:

    • Come from the same distribution
  3. If your Neural Network model seems to have high variance, what of the following would be promising things to try?

    • Add regularization
    • Get more training data

    Note: Check here.

  4. You are working on an automated check-out kiosk for a supermarket, and are building a classifier for apples, bananas and oranges. Suppose your classifier obtains a training set error of 0.5%, and a dev set error of 7%. Which of the following are promising things to try to improve your classifier? (Check all that apply.)

    • Increase the regularization parameter lambda
    • Get more training data

    Note: Check here.

  5. What is weight decay?

    • A regularization technique (such as L2 regularization) that results in gradient descent shrinking the weights on every iteration.
  6. What happens when you increase the regularization hyperparameter lambda?

    • Weights are pushed toward becoming smaller (closer to 0)
  7. With the inverted dropout technique, at test time:

    • You do not apply dropout (do not randomly eliminate units) and do not keep the 1/keep_prob factor in the calculations used in training
  8. Increasing the parameter keep_prob from (say) 0.5 to 0.6 will likely cause the following: (Check the two that apply)

    • Reducing the regularization effect
    • Causing the neural network to end up with a lower training set error
  9. Which of these techniques are useful for reducing variance (reducing overfitting)? (Check all that apply.)

    • Dropout
    • L2 regularization
    • Data augmentation
  10. Why do we normalize the inputs x?

    • It makes the cost function faster to optimize