Can you explain the mechanics of gradient descent? I'm curious about how this optimization algorithm functions in practice. What are its underlying principles, and how does it effectively minimize loss in machine learning models? Additionally, what are the potential pitfalls or limitations associated with relying on gradient descent for training algorithms?
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