Explain "Gradient Descent" and What It Does in a Neural

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The Answer is Gradient descent is the part of the neural network algorithm that adjusts the weights of neurons at each iteration.
More broadly, the gradient descent algorithm estimates how steeply error will be reduced or increased by a given change in a weight, then selects the change corresponding to the estimated steepest decline in error. The amount estimated by gradient descent is the "learning rate". While this process usually works well, gradient descent can lead to locally suboptimal minimization of error. To avoid suboptimal solutions, some software incorporates noise to jog the algorithm out of local minima.

Explain "Gradient Descent" and What It Does in a Neural

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