really so that's the gradient that we care about the gradient of our loss with respect to W 2 now to evaluate this we can just apply the chain rule in calculus so we can split this up into the gradient of our loss with respect to our output Y multiplied by the gradient of our walk or output Y with respect to W 2 now if we want to repeat this process for a different way in the neural network let's say now W 1 not W 2 now we replace W 1 on both sides we also apply the chain rule but now you're going to notice that the gradient of Y