Machine learning models contain a number of parameters, as a system of equations. If the number of parameters is too low, models do not afford to approximate data representation, called under-fitting. In contrast, over-fitting is striving to fit models which have a high number of parameters makes them lose their generalization. The optimum value is… Continue reading Some Deep Learning Regularization techniques
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