# Why does feature scaling improve the convergence speed for gradient descent?

We can speed up gradient descent by scaling. This is because θ will descend quickly on small ranges and slowly on large ranges, and so will oscillate inefficiently down to the optimum when the variables are very uneven.

For linear regresion, from the equation $$a_{n+1}=a_n-\alpha\nabla F(a_n)$$ it wasn't obvious to me why larger range variables converge slower or how/why an oscillation occurs.