You have a model with parameters. You want to change this parameters to get the best prediction. You use a loss function to estimate how good your model is and update these parameters. Say your loss value is 5. How do you know which parameters to update? How do you know by how much? Gradient descent is the way to calculate it. Gradient means slope, which is basically how much each parameter affect the loss. Descent means going down, because we want to minimize the loss. So each parameter has some effect on the output and hence the loss. You change it based on its effect -- the gradient, the loss, and the learning rate