Tag
gradient-descent
- Aug 27, 2026 Squared, Absolute, and Quantile Loss Compared by Their Gradients How the shape of a regression loss decides which observations move the fit, why the absolute loss has no derivative at zero, and what the pinball loss estimates instead of a mean. machine learning
- Jul 5, 2026 Feature Scaling and the Conditioning of Gradient Descent How per-feature magnitude sets the condition number of a loss surface, why that number fixes the iteration count of first-order methods, and what each scaler costs. machine learning
- Aug 15, 2025 Step Sizes and the Descent Lemma in Gradient Descent How the Lipschitz constant of the gradient bounds a usable step size, why 1/L is the natural choice for quadratic objectives, and which constants the convergence rates hide. machine learning