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Gradient-based optimization is a class of algorithms used in machine learning and optimization problems to minimize or maximize a function. These algorithms are particularly useful when the function is differentiable, meaning it has a gradient that can be computed. The gradient is a vector of partial derivatives that points in the direction of the steepest ascent of the function. By following the negative of the gradient, gradient-based optimization algorithms can efficiently find the minimum of a function.
One of the most well-known gradient-based optimization algorithms is gradient descent. In gradient descent, the algorithm
There are several variants of gradient descent, including stochastic gradient descent (SGD), mini-batch gradient descent, and
Gradient-based optimization algorithms are widely used in machine learning because they can handle large-scale problems and