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[D] Benefits of learning linear/non-linear optimzation

Hi Friends working in industry as deep/machine learning engineers,

Are there any benefits on having an extensive knowledge of optimization algorithms beyond the variations of gradient descent? (i.e. Chambolle-Pock, Split Bregman, Proximal methods, etc.)

I’m debating on spending my next semester taking a course either on this subject or deep learning for computer vision. I’d love to hear everyone’s opinion on which skills would be more relevant for industry.

Many thanks!

submitted by /u/jtschwar
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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.