Training loss: Difference between revisions

The training loss is used to evaluate the performance of a machine learning model during training. To minimize this loss, optimization algorithms such as stochastic gradient descent (SGD) or Adam are employed. These optimization processes modify the model's weights in order to minimize its training loss.