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Stanford CS229M: Machine Learning Theory - Fall 2021

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Stanford CS229M - Lecture 16: Implicit regularization in classification problems

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Stanford CS229M - Lecture 15: Implicit regularization effect of initialization

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Stanford CS229M - Lecture 14: Neural Tangent Kernel, Implicit regularization of gradient descent

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Stanford CS229M - Lecture 13: Neural Tangent Kernel

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Stanford CS229M - Lecture 11: All-layer margin

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Stanford CS229M - Lecture 10: Generalization bounds for deep nets

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Stanford CS229M - Lecture 9: Covering number approach, Dudley Theorem

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Stanford CS229M - Lecture 8: Refined generalization bounds for neural nets, Kernel methods

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Stanford CS229M - Lecture 7: Challenges in DL theory, generalization bounds for neural nets

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Stanford CS229M - Lecture 6: Margin theory and Rademacher complexity for linear models

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Stanford CS229M - Lecture 5: Rademacher complexity, empirical Rademacher complexity

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Stanford CS229M - Lecture 4: Advanced concentration inequalities

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Stanford CS229M - Lecture 3: Finite hypothesis class, discretizing infinite hypothesis space

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Stanford CS229M - Lecture 2: Asymptotic analysis, uniform convergence, Hoeffding inequality

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Stanford CS229M - Lecture 1: Overview, supervised learning, empirical risk minimization

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