now we calculate the gradient with respect to the input which if this is a linear function so if you have a live f of X equals WX like a linear function then this gradient del F del X would just give you the W will give you the slope of the linear function and the same in the neural network when you linearize it alright so we're going to obtain all these linearizations and that gives us the this matrix J right here and what we can do is we can then observe the covariance matrix of J of all these linearizations the covariance