problems they're outside of the scope of this tutorial but I'll just mention briefly how they can be related so in hyper parameter optimization you can say that theta refers to your hyper parameters that's where you're going to get out of your meta training set and Phi is the network weights so you'll learn your hyper parameters from d mehta train and then you'll use them to get Phi architecture search for same deal theta refers to the parameters of your architecture and Phi is the actual weights in the model this is a very