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