learning rate and trust each update more so if we're very noisy in our gradient estimation we probably want to lower our learning rate a little more so we don't fully step in the wrong direction if we're not totally confident with that gradient if we have a larger batch of gradient of data to they are gradients with we can trust that learning great a little more increase it so that it steps it more aggressively in that direction what this means also is that we can now massively paralyze this computation because we can