This is what's written:
So, $h(x)$ is basically a linear function to predict values of some training set. I understand everything that's happening up to the point where we take the partial derivative of $h(x)$ in summation form (the second equation above the black line), but how do we get the partial derivative of $h(x) - y$ to just $x_j$?
Also, why does the update in the equation under the black line change what's calculated before: $(h(x) - y)x_j$ to $(y - h(x))x_j$?
I'm sorry if I'm not being clear enough. Please let me know if that's the case.