大蜕

Coursera 第二阶段第一课 Quiz

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?

[√] Get more training data [√] Add regularization [x] Increase the number of units in each hidden layer [x] Make the Neural Network deeper [x] Get more test data

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 [x] Decrease the regularization parameter lambda [x] Use a bigger neural network

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:

TBD

8.Increasing the parameter keep_prob from (say) 0.5 to 0.6 will likely cause the following: (Check the two that apply)

[√] Causing the neural network to end up with a lower training set error [√] Reducing the regularization effect [x] Causing the neural network to end up with a higher training set error [x] Increasing the regularization effect

9.Which of these techniques are useful for reducing variance (reducing overfitting)? (Check all that apply.)

[√] Dropout [√] L2 regularization [√] Data augmentation [x] Xavier initialization [x] Vanishing gradient [x] Gradient Checking [x] Exploding gradient

10.Why do we normalize the inputs x?

It makes the cost function faster to optimize