APPLICATION OF SUPERVISED LEARNING
DEEP LEARNING
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Question
[CLICK ON ANY CHOICE TO KNOW THE RIGHT ANSWER]
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Offers regularization and helps build deeper networks
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Can help with uncertainty estimation through Monte-Carlo use
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Increases the capacity of the model
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Prevents vanishing gradients
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None of these
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Detailed explanation-1: -But why do this? Dropout is a regularization technique, that is, it helps prevent overfitting. With little data and/or a complex network, the model might memorize the training data and, as a result, work great on the data it has seen during training but deliver terrible results on new, unseen data.
Detailed explanation-2: -Dropout Regularization Hyperparameters Limiting our weight vectors using dropout allows us to employ a high learning rate without fear of the weights blowing up. Dropout noise, along with our big decaying learning rate, allows us to explore alternative areas of our loss function and, hopefully, reach a better minimum.
Detailed explanation-3: -The dropout layer indiscriminately culls a specified portion of neurons, decreasing the representational capacity of the model in question. This prevents the network from fitting complex nonlinear decision boundaries(i.e. the “noise” in the dataset), thus preventing(or ameliorating) overfitting. Save this answer.