Multivariable Functions and Partial Derivatives
An introduction to multivariable functions, coordinate slices, partial derivatives, and tangent-plane construction.
Gradient and Multidimensional Optimization
The gradient as local direction and rate of change, with applications to multidimensional optimization and regression.
Gradient Descent in One Variable
The one-variable gradient-descent update, learning-rate trade-offs, local minima, and a compact implementation example.
Gradient Descent in Two Variables
Extending gradient descent to two parameters through partial derivatives, vector updates, and convergence considerations.
Gradient Descent for Least Squares
Deriving and applying gradient-descent updates for the slope and intercept of a linear least-squares model.