[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.