into how we actually design these meta learning so the general recipe and the general principle behind these algorithms is that we need to choose some form of inferring the parameters of a model Phi given our chain data set and our meta parameters theta and then once we choose choose the form of this we can then optimize it optimize the meta parameters fight theta with respect to a maximum likelihood objective using our meta training data okay and many of the different algorithms that were gonna be looking at today really only differ in