APPLICATION OF SUPERVISED LEARNING
DEEP LEARNING
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Question
[CLICK ON ANY CHOICE TO KNOW THE RIGHT ANSWER]
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Softmax
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Max pooling
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Vanishing gradient
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Dropout
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Detailed explanation-1: -Simplifying The Model. The first step when dealing with overfitting is to decrease the complexity of the model. Early Stopping. Use Data Augmentation. Use Regularization. Use Dropouts. 06-Dec-2019
Detailed explanation-2: -Dropout is a technique for addressing this problem. The key idea is to randomly drop units (along with their connections) from the neural network during training. This prevents units from co-adapting too much. During training, dropout samples from an exponential number of different “thinned” networks.
Detailed explanation-3: -In machine learning, “dropout” refers to the practice of disregarding certain nodes in a layer at random during training. A dropout is a regularization approach that prevents overfitting by ensuring that no units are codependent with one another.