11 docs tagged with "optimization"

Binary classification with a linear score, sigmoid output, log loss, and chain-rule parameter gradients.

How derivatives support critical-point analysis, monotonicity, concavity, implicit differentiation, and Newton's method.

The gradient as local direction and rate of change, with applications to multidimensional optimization and regression.

Deriving and applying gradient-descent updates for the slope and intercept of a linear least-squares model.

The one-variable gradient-descent update, learning-rate trade-offs, local minima, and a compact implementation example.

Extending gradient descent to two parameters through partial derivatives, vector updates, and convergence considerations.

Why logarithmic loss measures probabilistic classification error and how calculus connects it to likelihood optimization.

A compact vocabulary for models, losses, empirical risk, likelihood, regularization, optimization, and generalization.

A bridge from calculus-based extrema to objective functions and machine-learning loss.

A calculus-based optimization workflow connecting objective functions, extrema, constraints, and applied modeling examples.

A single linear unit for regression, including squared loss, chain-rule gradients, and parameter updates.