C2W1_Quiz_Practical_aspects_of_deep_learning.txt

Quiz Week 1

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 bias, what of the following would be promising things to try? (Check all that apply.)
    Increase the number of units in each hidden layer
    Make the Neural Network deeper

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

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.)
    L2 regularization
    Dropout
    Data augmentation

10. Why do we normalize the inputs x?
    It makes the cost function faster to optimize