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Explores diverse regularization approaches, including the L0 quasi-norm and the Lasso method, discussing variable selection and efficient algorithms for optimization.
Explores advanced optimization techniques for machine learning models, focusing on adaptive gradient methods and their applications in non-convex optimization problems.
Explores data augmentation as a key regularization method in deep learning, covering techniques like translations, rotations, and artistic style transfer.
Explores optimization methods, including convexity, gradient descent, and non-convex minimization, with examples like maximum likelihood estimation and ridge regression.
Covers gradient descent methods for convex and nonconvex problems, including smooth unconstrained convex minimization, maximum likelihood estimation, and examples like ridge regression and image classification.
