Adam differs from classical stochastic gradient descent (SGD) primarily in its handling of the learning rate. SGD maintains a single, constant learning rate for all parameter updates during training. In contrast, Adam dynamically adapts the learning rate for each parameter. Adam achieves this by combining the features of gradient descent with momentum and RMSProp, utilizing both the moving average of the first moment (the mean) and the second moment (the uncentered variance) of the gradients.