What is Gradient Descent Algorithm?
The gradient descent algorithm is a strategy that helps refine machine learning. The gradient descent algorithm works towards adjusting the input weights of neurons in artificial neural networks and finding local minima or global minima to optimize a problem.
The gradient descent algorithm is also known simply as gradient descent.
To understand how gradient descent works, first think of a graph of predicted values next to a graph of actual values that may not follow a strictly predictable path. With the gradient lowering the prediction error or the gap between the theoretical values and the observed actual values or with machine learning, the training set, is reduced by adapting the input weights.
The algorithm calculates the gradient or change and gradually shrinks this predictive gap in order to refine the output of the machine learning system. Gradient descent is a popular way to refine the output of ANNs as we examine what they can do in all sorts of software areas.
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