As a network trains, it tends to learn more complex functions, and the discrepancy between the expected output and the actual one starts falling for training data. Even better, this discrepancy, known as loss, also starts going down for test data, which is new data not used in training. But at some point, the model starts to overfit, and while the loss on training data keeps falling, the test data’s loss starts to rise. So, typically, that’s when researchers stop training the network. That was t...