you can see here and I would say 95% of all supervised machine learning problems are framed just like that you have some kind of input which in our case of course is our video our sequence of images that goes into a model could be anything in our case that lets say some type of neural network that model spits out a prediction and we then compare the prediction to the label to the truth based on a difference based on the error that our model makes we're going to take a step in our optimization procedure for example using gradient descent and the