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If you have 10,000,000 examples, how would you split the train/dev/test set?
- 98% train . 1% dev . 1% test
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The dev and test set should:
- Come from the same distribution
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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.
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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.
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What is weight decay?
- A regularization technique (such as L2 regularization) that results in gradient descent shrinking the weights on every iteration.
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What happens when you increase the regularization hyperparameter lambda?
- Weights are pushed toward becoming smaller (closer to 0)
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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
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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
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Which of these techniques are useful for reducing variance (reducing overfitting)? (Check all that apply.)
- Dropout
- L2 regularization
- Data augmentation
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Why do we normalize the inputs x?
- It makes the cost function faster to optimize
