A Beginners Guide to the Gradient Descent Algorithm

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The gradient descent algorithm is an approach to find the minimum point or optimal solution for a given dataset. In Machine Learning, it is used to update the coefficients of our model. It also ensures that the predicted output value is close to the expected output. The weight updation takes place by decrementing the cost function in steps of the gradient (derivative) of weight function. In real-world cases, it moves in a zig-zag manner for most of the datasets.