Optimizers like Adam and RMSprop work by adjusting the weights of a neural network during training to minimize the loss function. RMSprop adapts the learning rate for each weight by dividing the gradient by a running average of recent gradient magnitudes, helping to stabilize updates and prevent large oscillations. This makes RMSprop effective for non-stationary problems, such as reinforcement learning.
Adam (Adaptive Moment Estimation) builds on RMSprop by incorporating momentum, which considers the moving averages of both gradients (first moment) and squared gradients (second moment). This dual mechanism ensures that Adam can adapt learning rates based on the direction and magnitude of updates, resulting in faster convergence and more stable training.
Both optimizers reduce the need for manual tuning of the learning rate, making them popular choices for a wide variety of tasks. Adam is particularly favored for its efficiency and robustness, while RMSprop is often used in specialized contexts like deep reinforcement learning.