APPLICATION OF SUPERVISED LEARNING
SUPERVISED AND UNSUPERVISED LEARNING
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Question
[CLICK ON ANY CHOICE TO KNOW THE RIGHT ANSWER]
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Regularization Weight
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Number of Hidden Nodes
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Learning Rate
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Number of Learning Iterations
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Detailed explanation-1: -Regularization is the technique in which slight modifications are made to learning algorithm such that the model generalizes better. This in turn results in the improvement of the model’s performance on the test data or unseen data. In weight regularization, It penalizes the weight matrices of nodes.
Detailed explanation-2: -Among these parameters are the number of layers, the number of neurons per layer, the number of training iterations, et cetera. Some of the more important parameters in terms of training and network capacity are the number of hidden neurons, the learning rate and the momentum parameter.
Detailed explanation-3: -according to the formula the number of model parameters(weights) of this Neural Network model = (2x2)+(2x2)+(2+2)=12.