Unverified50% confidenceFactExact time
Gradient descent computes the partial derivatives of the loss function with respect to model parameters and updates the parameters in the direction opposite to the gradient to approach the minimum of the loss function.
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8/29/2026
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UnverifiedGradient descent updates parameters through the iterative formula θ = θ - α∇J(θ), where α is the learning rate and ∇J(θ) is the vector of partial derivatives of the loss function with respect to the parameters.79% similarUnverifiedResearch by Gunasekar et al. in 2017 proved that for linear models, gradient descent is equivalent to minimum-norm interpolation.76% similarUnverifiedBayesian decision theory selects the action a* that minimizes expected loss, i.e., a* = argmin_a E[L(θ, a)|x], where the expectation is taken with respect to the posterior distribution p(θ|x).71% similarUnverifiedRidge regression shrinks regression coefficients toward zero but does not set them to zero by adding an L2 regularization term to the loss function, whereas LASSO (L1 regularization) produces sparse solutions.70% similarUnverified绝大多数常见损失函数都可以从最大似然估计推导出来70% similar
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