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The gradient is a fancy word for derivative, or the rate of change of a function. Itâs a vector (a direction to move) that. 1  Points in the direction of greatest increase of a function (intuition on why)  Is zero at a local maximum or local minimum (because there is no single direction of increase)
Gradient descent. Gradient descent is a first-order iterative optimization algorithm for finding the minimum of a function. To find a local minimum of a function using gradient descent, one takes steps proportional to the negative of the gradient (or of the approximate gradient) of the function at the current point. If instead one takes steps proportional to the positive of the gradient, one approaches a local maximum of that function; the procedure is then known as gradient ascent.