C2M1

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The dev and test set should:

• • Come from the same distribution

• • Come from the different distribution

• Be identical to each other (same (x, y) pairs)

• Nave the same number of example

If your Neural Network model seems to have high variance, what of the following would be promising things to try? (Check all that apply.)

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• Get more traininig data

• Get more test data

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

• Decrease the regularization parameter lambda

• Gt more training data

• Use a bigger neural network

What is weight decay?

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• • A regularization technique (such as L2 regularization) that results in gradient descent shrinking the weights on every iteration.

What happens when you increase the regularization hyperparameter lambda?

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• • Weights are pushed toward becoming smaller (closer to 0)

• • Weights are pushed toward becoming bigger (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)

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Which of these techniques are useful for reducing variance (reducing overfitting)? (Check all that apply.)

• Dropout

• L2 regularization

• Data augmentation

• Xavier initialization

• Gradient Checking

Why do we normalize the inputs x?

• It makes it easier to visualize the data

• It makes the parameter initialization faster

• It makes the cost function faster to optimize