CS 451 Quiz 31
Hyperparameter tuning and regularization
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Two of the following 4 terms are synonyms. Which two? *
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An important guideline is to make sure that dev set and test set come from the same distribution. *
The "basic recipe for machine learning" checks for *
The "basic recipe for machine learning" says we're done if the answers to (1) high bias? and (2) high variance? are: *
What is Dropout? *
How is "inverted dropout" implemented? Assume we use "keep_prob" P (e.g. P=0.8), so a fraction F = (1-P) of the nodes drop out (e.g. F = (1-P) = 20%). *
Do you use dropout at test time? *
Why does dropout work? *
Data augmentation: which statement is NOT true? *
Which topic was NOT discussed in the assigned videos? *
I'm having too much fun making up wrong answers...  :)
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