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For questions related to the concept of learning rate (of an optimization algorithm, such as gradient descent) in machine learning.

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It is known that if $\alpha$ is set to high, then the cost function of the model may not converge. However, would a decaying of the learning rate provide some "tuning" of the $\alpha$ value during t …
asked Jun 16 '20 by calveeen
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Adam is known as an algorithm that has an adaptive learning rate for each parameter. I believe this is due to the division by the term $$v_t = \beta_2 \cdot v_{t-1} + (1-\beta_2) \cdot g_t^2 $$ Hence, …
asked Jun 17 '20 by calveeen