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This question already has an answer here:

If a gradient points towards a max or a min what stops gradient descent from maximizing error instead of minimizing it?

Is it the nature of the update step that makes this process one way?

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marked as duplicate by Reinstate Monica, usεr11852 says Reinstate Monic, mdewey, kjetil b halvorsen, Robert Long Apr 24 at 8:21

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In gradient decent, we are following the negative gradient direction, where the objective function will decrease instead of increase.

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