gradient descent on an embedding that produces a set of parameters so this sort of combines all three approaches and then finally we can do something like mammal but initialize the last layer to be a linear classifier equivalent to the prototypical networks classifier okay before we move on to the takeaways are there any questions about nonparametric approaches or any questions from slide oh no there are roughly 200 questions and I can't sort through them all but there are a couple of questions that are kind