training deep leaning models note in generalan lp norm is defined as || || si | | ) / accordinglythe norm is defined as || || (si | | ) / si | similarlythe norm is defined as || || (si | | ) / (si ( ) ) / let' dive deeper into the regularized loss function lfnn(xth)ya (ththe following points are to be noted as we attempt to minimize the overall loss function lfnn(xth)ya (th)we attempt to reduce the contribution of the lfnn(xth)yterm as well as the regularization term given by (th it follows that for two sets of parameterstha and thbif lfnn(xtha)ylfnn(xthb) )then the optimization algorithm will choose tha if (thar(thband thb if (thar(thb thusthe role of the regularization term is to direct the optimization in the direction of the th that lowers (th it is easy to see that lower values of (thwhen corresponds to regularization will lead to sparser thhence reducing the effective capacity it is easy to see that lower values of (thwhen corresponds to regularization will lead to th closer to hence reducing the effective capacity (see figure - the term is used to control how much emphasis we place on lfnn(xth)yversus (thhigher values of mean more emphasis is placed on regularization