adaptive learning rates in gradient descent
Adaptive learning rates in gradient descent refer to the practice of adjusting the size of the steps taken during the optimization process. Instead of using a fixed learning rate, it dynamically changes based on the characteristics of the data being processed. This adaptive approach enhances the efficiency of the gradient descent algorithm by allowing it to take larger steps in flat areas and smaller steps in steep areas, effectively reaching the optimal solution faster.
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