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Deep Learning: Conv Nets

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  • 1. Multiple Choice
    1 minute
    1 pt

    How is shift Invariance achieved in ConvNets?

    Through convolutional equivariance

    Through convolutional equivariance and approximate translation invariance with pooling

    Through convolutional equivariance and exact pooling invariance

    They exist in a higher dimensional invariant space

  • 2. Multiple Choice
    45 seconds
    1 pt

    Why do we include dropout in the network architecture ?

    Offers regularization and helps build deeper networks

    Can help with uncertainty estimation through Monte-Carlo use

    Increases the capacity of the model

    Prevents vanishing gradients

    None of these

  • 3. Multiple Choice
    1 minute
    1 pt

    Model Ensembling is:

    Having multiple instances of the network(s) and average together their responses

    Having a single instance of the network and pass the input multiple times but altered in a small way

    The perfect string quartet

    None of the above

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