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What is the difference between momentum and learning rate?

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This way, even if the algorithm is stuck in a flat region, or a small local minimum, it can get out and continue towards the true minimum. read more

In summary: when performing gradient descent, learning rate measures how much the current situation affects the next step, while momentum measures how much past steps affect the next step. read more

Usually you should start with a high learning rate and a low momentum. Then you decrease the learning rate over time and increase the momentum. The idea is to allow more exploration at the beginning of the learning and force convergence at the end of the learning. read more

The learning rate is a parameter that determines how much an updating step influences the current value of the weights. While weight decay is an additional term in the weight update rule that causes the weights to exponentially decay to zero, if no other update is scheduled. read more

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