# What's learning rate?

The learning rate is an important hyperparameter that controls how quickly the model is adapted to the problem during the training. It can be seen as the “step width” during the parameter updates, i.e. how far the weights are moved into the direction of the minimum of our optimization problem.

A large learning rate can accelerate the training. However, it is possible that we “shoot” too far and miss the minimum of the function that we want to optimize, which will not result in the best solution. On the other hand, training with a small learning rate takes more time but it is possible to find a more precise minimum. The downside can be that the solution is stuck in a local minimum, and the weights won’t update even if it is not the best possible global solution.

There is no straightforward way of finding an optimum learning rate for a model. It involves a lot of hit and trial. Usually starting with a small values such as 0.01 is a good starting point for setting a learning rate and further tweaking it so that it doesn’t overshoot or converge too slowly.