In this context, it refers to the gradient of the error function (which is a function of the parameters). In other words, for each tuple of parameters you can

In this context, it refers to the gradient of the error function (which is a function of the parameters). In other words, for each tuple of parameters you can estimate the direction in which the reduction in the resulting error would be steepest. Gradient descent is then a method whereby you iteratively move towards better and better parameterisations, by following the direction of the (negative) gradient at each step, which guarantees you will reach a local minimum much more efficiently than if you sampled new parameters in the area of the previous iteration at random. And if your error function is convex, the minimum error is a global minimum.