Скачать или смотреть What is Perceptron Loss Function | Hinge Loss Function

In this video, we demystify the Perceptron loss and Hinge loss—two classic margin-based objectives used with linear classifiers. You’ll learn what each loss measures, when it triggers updates, how the gradients/subgradients look, and why SVMs prefer hinge loss while the classic Perceptron uses a simpler mistake-driven rule. Perfect for students, ML beginners, and interview prep.

Key takeaways
Perceptron = mistake-driven: updates only on misclassification
Hinge = margin-aware: penalizes low-confidence correct predictions too
SVM = hinge loss + regularization → better generalization on tough data

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