be known as pre training then you take this network and and it's sort of converged to some set of parameters on taos k and then you train it again this time on task B and then voila you have your final model that you're going to deploy and it's it's hopefully going to do great on on task B so this paradigm is pretty simple but it's been extremely successful and it's probably the computer vision community that sort of really showed us how successful this could be in various applications so in sort of specifically the current setting