Optimization techniques in the Machine Learning module. covered the fundamentals of - - SGD variants - Convex vs Non-Convex - Momentum & Nesterov acc. - Adam, RMSprop - Loss landscapes - Regularization - Batch Norm - Newton's method & Lagrange -/tensortonic.com/ml-math | Md Ismail Sojal
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Convex Optimization I | Course | Stanford Online
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