The basic idea behind gradient descent is to take repeated steps in the direction that reduces the value of the function the most. For a machine learning model, this might involve adjusting parameters to minimize errors in predictions. The size of these steps is determined by the learning rate, a hyperparameter that needs careful tuning.
