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    Coursera
    Natural Language Processing in TensorFlow
    Week 4
    Week 4 Quiz
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      • Completed
        Video: LectureA conversation with Andrew Ng
        . Duration: 1 minute1 min
      • Completed
        Video: LectureIntroduction
        . Duration: 1 minute1 min
      • Completed
        Video: LectureLooking into the code
        . Duration: 57 seconds57 sec
      • Completed
        Video: LectureTraining the data
        . Duration: 2 minutes2 min
      • Completed
        Video: LectureMore on training the data
        . Duration: 1 minute1 min
      • Completed
        Reading: Check out the code!
        . Duration: 10 minutes10 min
      • Completed
        Video: LectureNotebook for lesson 1
        . Duration: 8 minutes8 min
      • Completed
        Video: LectureFinding what the next word should be
        . Duration: 2 minutes2 min
      • Completed
        Video: LectureExample
        . Duration: 1 minute1 min
      • Completed
        Video: LecturePredicting a word
        . Duration: 1 minute1 min
      • Completed
        Video: LecturePoetry!
        . Duration: 40 seconds40 sec
      • Completed
        Reading: link to Laurence's poetry
        . Duration: 10 minutes10 min
      • Completed
        Video: LectureLooking into the code
        . Duration: 1 minute1 min
      • Completed
        Video: LectureLaurence the poet!
        . Duration: 1 minute1 min
      • Completed
        Reading: Check out the code!
        . Duration: 10 minutes10 min
      • Completed
        Video: LectureYour next task
        . Duration: 1 minute1 min
      • Completed
        Reading: Link to generating text using a character-based RNN
        . Duration: 10 minutes10 min
      • Completed
        Quiz: Week 4 Quiz
        8 questions
    Quiz

    Week 4 Quiz

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    Due DateJan 11, 2:59 PM +07
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    Week 4 Quiz
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    Due Jan 11, 2:59 PM +07

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    Week 4 Quiz

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    1.
    Question 1

    What is the name of the method used to tokenize a list of sentences?

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    2.
    Question 2

    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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    3.
    Question 3

    What is the purpose of the embedding dimension?

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    4.
    Question 4

    IMDB Reviews are either positive or negative. What type of loss function should be used in this scenario?

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    5.
    Question 5

    If you have a number of sequences of different lengths, how do you ensure that they are understood when fed into a neural network?

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    6.
    Question 6

    When predicting words to generate poetry, the more words predicted the more likely it will end up gibberish. Why?

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    7.
    Question 7

    What is a major drawback of word-based training for text generation instead of character-based generation?

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    8.
    Question 8

    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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