from data in a very creative way and see what kind of features the model can learn and there we are gonna look at three different techniques relative patch prediction jigsaw puzzles and rotation and finally we are going to look at contrast learning which is really the version of supervised or unsupervised learning that's been taking off very very recently and we're going to look at a foundational work over to back which explains a lot of these foundational ideas like nice contrast loss and then we're going to look at a