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Prompt
"Explain why gradient descent can get stuck in local minima."
Response A
Gradient descent iterates by moving in the direction of steepest descent. When the loss surface has local minima, the algorithm can converge there instead of the global minimum...
Response BPreferred
The loss landscape is non-convex for neural networks. Gradient descent follows local gradients, so once it enters a "valley" it has no signal to escape. The gradient is zero, so updates stop...
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Aanshit A.
USC · CS Junior
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