have a huge impact in performance so if you set that learning rate to small that means that you're not really trusting your gradient on each step so if ADA is super tiny that means on each time each step you're only going to move a little bit towards in the opposite direction of your gradient just in little small increments and what can happen then is you can get stuck in these local minima because you're not being as aggressive as you should be to escape them now if you set the learning rate to large you can