April 2018
Intermediate to advanced
334 pages
10h 18m
English
The cost function is a metric that determines how well or poorly a machine learning algorithm performed with regards to the actual training output and the predicted output. If you remember linear regression, where the sum of squares of errors was used as the loss function, that is,
. This works better in a convex curve, but in the case of classification, the curve is non convex; as a result, the gradient descent doesn't work well and doesn't tend to global optimum. Therefore, we use cross-entropy loss which fits better in classification tasks as the cost function.