Delta rule vs. gradient descent?

Question

What's the difference between gradient descent and the delta rule?

Answer 1

Without math: The delta rule uses gradient descent to minimize the error from a perceptron network's weights.

Gradient descent is a general algorithm that gradually changes a vector of parameters in order to minimize an objective function. It does this by moving in the direction of least resistance, i.e. the direction that has the largest (negative) gradient.

You find this direction by taking the derivative of the objective function. It's like dropping a marble in a smooth hilly landscape. It guaranties a local minimum only. So, the short answer is that the delta rule is a specific algorithm using the general algorithm gradient descent.