In Depth
Gradient descent is the primary optimization algorithm used to train machine learning models. It works by computing the gradient (slope) of the error function with respect to each model parameter, then updating those parameters by stepping in the direction that reduces the error. Think of it like finding the lowest point in a valley by always walking downhill.
There are several variants of gradient descent. Batch gradient descent computes gradients over the entire dataset, stochastic gradient descent (SGD) uses individual samples, and mini-batch gradient descent splits the difference by using small batches. Modern variants like Adam, AdaGrad, and RMSprop add momentum and adaptive learning rates to navigate complex error landscapes more efficiently.
The learning rate hyperparameter controls how large each step is: too large and the model overshoots the optimal solution, too small and training becomes prohibitively slow. Finding the right learning rate and choosing the right optimizer variant are critical decisions in model training that significantly impact both final performance and training efficiency.