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    Coursera
    Natural Language Processing in TensorFlow
    Week 3
    Week 3 Quiz
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      • Completed
        Video: LectureA conversation with Andrew Ng
        . Duration: 2 minutes2 min
      • Completed
        Video: LectureIntroduction
        . Duration: 2 minutes2 min
      • Completed
        Reading: Link to Andrew's sequence modeling course
        . Duration: 10 minutes10 min
      • Completed
        Video: LectureLSTMs
        . Duration: 2 minutes2 min
      • Completed
        Reading: More info on LSTMs
        . Duration: 10 minutes10 min
      • Completed
        Video: LectureImplementing LSTMs in code
        . Duration: 1 minute1 min
      • Completed
        Reading: Check out the code!
        . Duration: 10 minutes10 min
      • Completed
        Video: LectureAccuracy and loss
        . Duration: 1 minute1 min
      • Completed
        Video: LectureA word from Laurence
        . Duration: 35 seconds35 sec
      • Completed
        Video: LectureLooking into the code
        . Duration: 1 minute1 min
      • Completed
        Video: LectureUsing a convolutional network
        . Duration: 1 minute1 min
      • Completed
        Reading: Check out the code!
        . Duration: 10 minutes10 min
      • Completed
        Video: LectureGoing back to the IMDB dataset
        . Duration: 1 minute1 min
      • Completed
        Reading: Check out the code!
        . Duration: 10 minutes10 min
      • Completed
        Video: LectureTips from Laurence
        . Duration: 37 seconds37 sec
      • Completed
        Reading: Exploring different sequence models
        . 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

    Why does sequence make a large difference when determining semantics of language?

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    2.
    Question 2

    How do Recurrent Neural Networks help you understand the impact of sequence on meaning?

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    3.
    Question 3

    How does an LSTM help understand meaning when words that qualify each other aren’t necessarily beside each other in a sentence?

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    4.
    Question 4

    What keras layer type allows LSTMs to look forward and backward in a sentence?

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    5.
    Question 5

    What’s the output shape of a bidirectional LSTM layer with 64 units?

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    6.
    Question 6

    When stacking LSTMs, how do you instruct an LSTM to feed the next one in the sequence?

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    7.
    Question 7

    If a sentence has 120 tokens in it, and a Conv1D with 128 filters with a Kernal size of 5 is passed over it, what’s the output shape?

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    8.
    Question 8

    What’s the best way to avoid overfitting in NLP datasets?

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