APPLICATION OF SUPERVISED LEARNING
NEURAL NETWORK
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Question
[CLICK ON ANY CHOICE TO KNOW THE RIGHT ANSWER]
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Which of the following statements about dropout are correct?
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Dropout prevents complex ‘’co-adaption” where the model relies on all the features of the input.
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Dropout is active during training and testing.
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Dropout can be viewed as a form of ensemble learning
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The amount of dropout, p, can be optimised through standard stochastic gradient descent (SGD) methods
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Explanation:
Detailed explanation-1: -Dropout is a regularization technique for neural network models proposed by Srivastava et al. in their 2014 paper “Dropout: A Simple Way to Prevent Neural Networks from Overfitting” (download the PDF). Dropout is a technique where randomly selected neurons are ignored during training. They are “dropped out” randomly.
Detailed explanation-2: -Ensemble learning is using multiple models are trained and their prediction is combined. Dropout is dropping off (making zero) units in the neural network randomly with certain probability while training which results into a number of different neural networks.
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