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    Coursera
    Convolutional Neural Networks in TensorFlow
    Week 3
    Week 3 Quiz
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      • Completed
        Video: LectureA conversation with Andrew Ng
        . Duration: 3 minutes3 min
      • Completed
        Video: LectureUnderstanding transfer learning: the concepts
        . Duration: 2 minutes2 min
      • Completed
        Reading: Start coding!
        . Duration: 10 minutes10 min
      • Completed
        Video: LectureCoding transfer learning from the inception mode
        . Duration: 1 minute1 min
      • Completed
        Reading: Adding your DNN
        . Duration: 10 minutes10 min
      • Completed
        Video: LectureCoding your own model with transferred features
        . Duration: 2 minutes2 min
      • Completed
        Reading: Using dropouts!
        . Duration: 10 minutes10 min
      • Completed
        Video: LectureExploring dropouts
        . Duration: 1 minute1 min
      • Completed
        Reading: Applying Transfer Learning to Cats v Dogs
        . Duration: 10 minutes10 min
      • Completed
        Video: LectureExploring Transfer Learning with Inception
        . Duration: 1 minute1 min
      • Completed
        Reading: What have we seen so far?
        . Duration: 10 minutes10 min
      • Quiz
        Quiz: Week 3 Quiz
        8 questions
      • Completed
        Video: LectureWeek 3 Wrap up
        . Duration: 36 seconds36 sec
    Quiz • 30 min

    Week 3 Quiz

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    Due DateJan 4, 2:59 PM +07
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    Week 3 Quiz
    Graded Quiz • 30 min

    Due Jan 4, 2:59 PM +07

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    Week 3 Quiz

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    1.
    Question 1

    If I put a dropout parameter of 0.2, how many nodes will I lose?

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    2.
    Question 2

    Why is transfer learning useful?

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    3.
    Question 3

    How did you lock or freeze a layer from retraining?

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    4.
    Question 4

    How do you change the number of classes the model can classify when using transfer learning? (i.e. the original model handled 1000 classes, but yours handles just 2)

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    5.
    Question 5

    Can you use Image Augmentation with Transfer Learning Models?

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    6.
    Question 6

    Why do dropouts help avoid overfitting?

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    7.
    Question 7

    What would the symptom of a Dropout rate being set too high?

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    8.
    Question 8

    Which is the correct line of code for adding Dropout of 20% of neurons using TensorFlow

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