> For the complete documentation index, see [llms.txt](https://vikram-bajaj.gitbook.io/machine-learning-stanford-coursera/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://vikram-bajaj.gitbook.io/machine-learning-stanford-coursera/supervised-learning/linear-regression/multivariate-linear-regression/choosing-the-learning-rate.md).

# Choosing the Learning Rate α

If $$α$$ is sufficiently small, J($$θ$$) will decrease with every iteration. This is how we know for sure that the gradient descent is working.

However, if the gradient descent is taking too long to converge, $$α$$ may be too small.

On the other hand, if $$α$$ is too large, we may overshoot the minimum, causing the cost function to increase instead of decreasing.

So, to choose an efficient learning rate, plot a graph taking J($$θ$$) on the Y-axis and # of iterations on the X-axis for different values of $$α$$ (Say 0.01, 0.03, 0.1, 0.3, 1 i.e. increasing threefold) to see which value of $$α$$ makes the gradient descent converge the quickest.