Gradient Descent

Gradient descent is an optimization algorithm used to minimize the error or loss function of a machine learning model by iteratively adjusting the model parameters in the direction of the steepest descent. It is widely used in training supervised learning models, such as linear regression, logistic regression, and neural networks, by updating weights or coefficients to find the optimal values that minimize prediction errors. Readers can explore gradient descent variants, optimization techniques, and convergence properties for improving training efficiency and model convergence in machine learning workflows, understanding its role in model optimization and parameter tuning.