Van klassieke optimalisatie naar stochastische gradiëntdaling

Machine learning optimization evolved from traditional calculus-based gradient descent to its stochastic variant due to computational efficiency demands. Stochastic Gradient Descent processes smaller data batches instead of entire datasets, enabling faster training on large-scale problems while maintaining convergence quality.