吴恩达深度学习学习笔记——C2W1——神经网络优化基础及正则化——练习题

C2W1 Quiz - Practical aspects of deep learning

Ans: C

Ans: A (Come from the same distribution)

Ans: C、D

 

Ans: A、C

Note: refer below diagram

Ans: A

Ans: A

Ans: D

Ans: B、D

Ans: B、E、G (Data augmentation, L2 regularization, Dropout)

Ans: B

 

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
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.)
   • Dropout
   • L2 regularization
   • Data augmentation
10. Why do we normalize the inputs x?
    • It makes the cost function faster to optimize