Loss Functions In Deep Learning Training Ppt

Slide 1

This slide lists the types of loss functions as a component of Deep Learning. These include mean absolute error, mean squared error, hinge loss, and cross-entropy.

Slide 2

This slide states that mean absolute error is a statistic for calculating the absolute difference between expected and actual values. Divide the total of all absolute differences by the number of observations. It does not penalize large values as harshly as Mean Squared Error (MSE).

Slide 3

This slide describes that MSE is determined by summing the squares of the difference between expected and actual values and dividing by the number of observations. It is necessary to pay attention when the metric value is higher or lower. It is only applicable when we have unexpected values for forecasts. We cannot rely on MSE since it might increase while the model performs well.

Slide 4

This slide explains that hinge loss function is commonly seen in support vector machines. The function has the shape = max[0,1-yf(x)]. When yf(x)>=0, the loss function is 0, but when yf(x)<0 the error rises exponentially, penalizing misclassified points that are far from the margin disproportionately. As a result, the inaccuracy would grow exponentially to those points.

Slide 5

This slide states that cross-entropy is a log function that predicts values ranging from 0 to 1. It assesses the effectiveness of a classification model. As a result, when the value is 0.010, the cross-entropy loss is more significant, and the model performs poorly on prediction.