for a wide variety of downstream tasks and we're also going to ask ourselves the question of what are these various pretext or proxy tasks that can be used to learn representations from raw unlabeled data and if we are able to learn good representations how can we leverage that and improve the data efficiency and performance of downstream tasks with a good pre training model so here is a figure from in Goodfellows deep learning textbook the focus here is how do we learn good representations and here's a simple case study of why representations