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    Coursera
    Sequences, Time Series and Prediction
    Week 2
    Week 2 Quiz
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      • Completed
        Video: LectureA conversation with Andrew Ng
        . Duration: 1 minute1 min
      • Completed
        Video: LecturePreparing features and labels
        . Duration: 4 minutes4 min
      • Completed
        Video: LecturePreparing features and labels
        . Duration: 3 minutes3 min
      • Completed
        Reading: Preparing features and labels notebook
        . Duration: 10 minutes10 min
      • Completed
        Reading: Sequence bias
        . Duration: 10 minutes10 min
      • Completed
        Video: LectureFeeding windowed dataset into neural network
        . Duration: 2 minutes2 min
      • Completed
        Video: LectureSingle layer neural network
        . Duration: 2 minutes2 min
      • Completed
        Video: LectureMachine learning on time windows
        . Duration: 37 seconds37 sec
      • Completed
        Video: LecturePrediction
        . Duration: 2 minutes2 min
      • Completed
        Video: LectureMore on single layer neural network
        . Duration: 2 minutes2 min
      • Completed
        Reading: Single layer neural network notebook
        . Duration: 10 minutes10 min
      • Completed
        Video: LectureDeep neural network training, tuning and prediction
        . Duration: 4 minutes4 min
      • Completed
        Video: LectureDeep neural network
        . Duration: 3 minutes3 min
      • Completed
        Reading: Deep neural network notebook
        . Duration: 10 minutes10 min
      • Warning
        Quiz: Week 2 Quiz
        9 questions
      • Completed
        Reading: Week 2 Wrap up
        . Duration: 10 minutes10 min
    Quiz

    Week 2 Quiz

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    Due DateDec 28, 2:59 PM +07
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    Week 2 Quiz
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    Due Dec 28, 2:59 PM +07

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    Week 2 Quiz

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    1.
    Question 1

    What is a windowed dataset?

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    2.
    Question 2

    What does ‘drop_remainder=true’ do?

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    3.
    Question 3

    What’s the correct line of code to split an n column window into n-1 columns for features and 1 column for a label

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    4.
    Question 4

    What does MSE stand for?

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    5.
    Question 5

    What does MAE stand for?

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    6.
    Question 6

    If time values are in time[], series values are in series[] and we want to split the series into training and validation at time 1000, what is the correct code?

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    7.
    Question 7

    If you want to inspect the learned parameters in a layer after training, what’s a good technique to use?

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    8.
    Question 8

    How do you set the learning rate of the SGD optimizer? 

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    9.
    Question 9

    If you want to amend the learning rate of the optimizer on the fly, after each epoch, what do you do?

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