Gradient descent is an optimization algorithm that is used to minimize the cost function of a machine learning algorithm. Gradient descent is called an iterative optimization algorithm because, in a stepwise looping fashion, it tries to find an approximate solution by basing the next step off its present step until a terminating condition is reached that ends the loop.
Take the following convex function in Figure 16-1 as a visual of gradient descent finding the minimum point of a function space.
Figure 16-1
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