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The Dual Problem of SVM
An in-depth exploration of the dual problem in SVMs, covering its mathematical foundation, Lagrangian formulation, duality principles, and complementary slackness for intuitive understanding.
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Subgradient and Subgradient Descent
An deep dive into subgradients, subgradient descent, and their application in optimizing non-differentiable functions like SVMs.
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Support Vector Machines(SVM) - From Hinge Loss to Optimization
Demystifying Support Vector Machines (SVM) - A step-by-step exploration of hinge loss, optimization, and gradient mechanics.
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Understanding the Maximum Margin Classifier
An engaging walkthrough of maximum margin classifiers, exploring their foundations, geometric insights, and the transition to support vector machines.
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L1 and L2 Regularization - Nuanced Details
A detailed explanation of L1 and L2 regularization, focusing on their theoretical insights, geometric interpretations, and practical implications for machine learning models.