second-order methods in gradient descent
Second-order methods in gradient descent refer to optimization algorithms that utilize information from the second derivative of the objective function to enhance the convergence speed. By incorporating this additional information, these methods can better approximate the location of the minimum and significantly improve the efficiency of the optimization process.
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