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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