Adaptive learning rate methods are optimization algorithms that adjust the learning rate dynamically during training, improving convergence speed and stability in deep learning models. These methods help in overcoming challenges like vanishing or exploding gradients by scaling the learning rate based on the magnitude of past gradients or parameter updates.