A learning rate is a hyperparameter that controls how much the weights of a model are adjusted after each iteration of training. It determines how quickly or slowly a model converges on a solution.
A learning rate that is too small will result in a slow convergence, meaning that the model will take a long time to reach an optimal solution. On the other hand, a learning rate that is too large can cause the model to diverge and never reach an optimal solution.
For example, if we are training a model on a dataset to classify images, a learning rate that is too large can cause the model to overfit the data and produce inaccurate results. On the other hand, a learning rate that is too small can cause the model to underfit the data and produce poor results. The best learning rate for a model depends on the dataset and the model itself.