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Neural Tangent Kernel (NTK) describes how wide neural networks train like kernel machines, turning gradient descent into kernel regression in the infinite-width limit.
The Jacobian matrix collects all first-order partial derivatives of a vector-valued function, describing how small input changes linearly affect each output component.