AI / ML

Gradient descent is the backbone of most ML training algorithms, but its variants—SGD, Adam, RMSprop—address specific challenges. For noisy datasets, Adam shines with adaptive learning rates, while SGD with momentum helps escape saddle points. RMSprop excels in scenarios with exploding gradients. The choice of optimizer, coupled with proper hyperparameter tuning, can drastically affect model convergence and performance. Always experiment to find the best fit for your problem.