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NND18 | C4 | Perceptron Convergence Theorem Explained (Part 5)
In this video we explain the Perceptron Convergence Theorem: why the perceptron learning algorithm is guaranteed to converge for linearly separable data. We carefully build the intuition using: - Dot products as alignment - The concept of margin δ - Lower bound vs upper bound on ‖x(k)‖² - The Cauchy–Schwarz inequality - The “squeeze ...
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