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Gradient descent is a powerful optimization technique used in machine learning and data science. It has been shown to be effective in many different scenarios and is often the go-to method for optimizing complex models.
Despite its power, gradient descent is actually quite simple to understand and use. The basic idea is that you start with an initial guess at the solution to your problem, then iteratively improve on that guess by taking into account the derivative of your function (the gradient) at each point. This allows you to move in the direction of the greatest improvement, making it a very efficient way to find optimal solutions.
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