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They have a few similarities, but they are quite different. Let me first give you a general description of both approaches/algorithms, so that you start to get a sense of their differences and similarities. Description Gradient descent (GD) can be applied to solving any optimization problem where your loss (aka cost or objective) function is differentiable ...


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