Is the learning rate the same as training speed?
No. It controls update size rather than hardware throughput. A larger learning rate may reach a useful solution sooner, or it may destabilize training entirely.
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Smaller updates can help refine parameters after larger improvements have been made. Whether this helps depends on the optimizer, schedule, and observed validation performance.
No. Adam adapts updates using gradient statistics, but its overall learning-rate setting and other hyperparameters can still strongly affect stability and final results.
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