Figure 1: Gradient descent with different learning rates
Gradient Descent is defined mathematically as:
$$ x_{n+1} = x_n - \alpha * \frac {d f(x)}{dx_n} $$
Where $\alpha$ is learning rate. Learning rate is a hyperparameter. Value of learning rate controls the steps size. Having a good learning rate is important because very low value of learning rate may take a long time to converge and having a very high value may not converge at all.
In Figure 1 we can see the impact of different learning rates on gradient descent.
Below program shows different learning rates for gradient descent using Tensorflow library.