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    Coursera
    Sequences, Time Series and Prediction
    Week 3
    Week 3 Quiz
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      • Completed
        Video: LectureWeek 3 - A conversation with Andrew Ng
        . Duration: 3 minutes3 min
      • Completed
        Video: LectureConceptual overview
        . Duration: 2 minutes2 min
      • Completed
        Video: LectureShape of the inputs to the RNN
        . Duration: 2 minutes2 min
      • Completed
        Video: LectureOutputting a sequence
        . Duration: 1 minute1 min
      • Completed
        Video: LectureLambda layers
        . Duration: 1 minute1 min
      • Completed
        Video: LectureAdjusting the learning rate dynamically
        . Duration: 2 minutes2 min
      • Completed
        Reading: More info on Huber loss
        . Duration: 10 minutes10 min
      • Completed
        Video: LectureRNN
        . Duration: 1 minute1 min
      • Completed
        Reading: RNN notebook
        . Duration: 10 minutes10 min
      • Completed
        Video: LectureLSTM
        . Duration: 1 minute1 min
      • Completed
        Reading: Link to the LSTM lesson
        . Duration: 10 minutes10 min
      • Completed
        Video: LectureCoding LSTMs
        . Duration: 2 minutes2 min
      • Completed
        Video: LectureMore on LSTM
        . Duration: 1 minute1 min
      • Completed
        Reading: LSTM notebook
        . Duration: 10 minutes10 min
      • Completed
        Quiz: Week 3 Quiz
        8 questions
      • Completed
        Reading: Week 3 Wrap up
        . Duration: 10 minutes10 min
    Quiz

    Week 3 Quiz

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    Due DateJan 4, 2:59 PM +07
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    Week 3 Quiz
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    Due Jan 4, 2:59 PM +07

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    Week 3 Quiz

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    1.
    Question 1

    If X is the standard notation for the input to an RNN, what are the standard notations for the outputs?

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    2.
    Question 2

    What is a sequence to vector if an RNN has 30 cells numbered 0 to 29

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    3.
    Question 3

    What does a Lambda layer in a neural network do?

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    4.
    Question 4

    What does the axis parameter of tf.expand_dims do?

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    5.
    Question 5

    A new loss function was introduced in this module, named after a famous statistician. What is it called?

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    6.
    Question 6

    What’s the primary difference between a simple RNN and an LSTM

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    7.
    Question 7

    If you want to clear out all temporary variables that tensorflow might have from previous sessions, what code do you run?

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    8.
    Question 8

    What happens if you define a neural network with these two layers?

    tf.keras.layers.Bidirectional(tf.keras.layers.LSTM(32)),

    tf.keras.layers.Bidirectional(tf.keras.layers.LSTM(32)),

    tf.keras.layers.Dense(1),

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