neural network with a step that's actually optimizing for the parameters in this case through gradient descent so we'll sample a task sample destroy its data sets for that and for parameters with gradient descent on the training data and then update our meta parameters using the test data points note that this does bring up a second-order optimization problem because you have to compute the gradient in step four you have to differentiate through a gradient step and test in in step three in practice a number of standard auto