## 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](https://user-images.githubusercontent.com/14886380/29240263-f7c517ca-7f93-11e7-8549-58856e0ed12f.png). 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](https://user-images.githubusercontent.com/14886380/29240263-f7c517ca-7f93-11e7-8549-58856e0ed12f.png). 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