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[D] Second order gradient optimization vs ADAM/momentum

I’m having trouble wrapping my head around how optimisers like ADAM and Momentum differ from second-order optimization methods.

The latter involves calculating/approximating the Hessian however the momentum based optimisers adjust their gradients from past steps (which is quite similar to how higher order derivatives work).

I know that mathematically and implementation-wise these two methods are different however can anyone provide any intuition as to how they differ in practice – perhaps by giving an example of where you would expect wildly different results from these two types of optimisers.

Thanks 🙂

submitted by /u/mellow54
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Toronto AI is a social and collaborative hub to unite AI innovators of Toronto and surrounding areas. We explore AI technologies in digital art and music, healthcare, marketing, fintech, vr, robotics and more. Toronto AI was founded by Dave MacDonald and Patrick O'Mara.