How do you calculate the learning rate for a machine learning algorithm?

The learning rate in a machine learning algorithm isn’t usually calculated directly — it’s chosen and tuned. It defines how big a step the model takes while updating weights.

To find a good learning rate, common approaches include:

Trial and tuning: Start with a small value (e.g., 0.01) and adjust.

Learning rate schedules: Automatically reduce over time.

Learning-rate finder: Test a range of rates and select the best based on loss behavior.

A well-chosen learning rate helps the model converge faster without overshooting or getting stuck.

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