difference between cross entropy error and accuracies why is the loss function compared to the overfitting error why is it so important to overfit a cross entropy model why does the average prediction error of the test curve have two different results why use a similar loss function for a test and a train which of these is true for cross entropy errors? is there a correlation error in a traing test what is the difference of training and test for overfitting which of the following is used to describe a cross-entropy error in a test? what is the cross entropy error what kind of error does an overfitting test give what type of accuracy is a _cross-entropy error in a convolutional neural network? how to compare test and train using cross entropy what's a similarity in cross entropy which error is the same across a training and testing set? why are similar cross entropy data stored why is it called cross entropy error when testing a similar model of training and testing why is the cross-entropy error in imagenet different than the accuracies in the traing test why isn't an overfitting loss function the same? why do different crossentropy tests have different results