Constant Learning Rate for Gradient Decent
Intuitively, if $\alpha$ is too large you may "shoot over" your target and end up bouncing around the search space without converging. If $\alpha$ is too small your convergence will be slow and you could end up stuck on a plateau or a local minimum. 

That's why most learning rate schemes start with somewhat larger learning rates for quick gains and then reduce the learning rate gradually.