ADAM, which stands for Adaptive Moment Estimation, is an optimization algorithm commonly used in machine learning and deep learning. It combines the advantages of two other popular algorithms, Momentum and RMSProp, to improve the training of neural networks. ADAM adjusts the learning rate for each parameter individually, making it efficient for large datasets and high-dimensional spaces. The algorithm maintains two moving averages: one for the gradients and another for the squared gradients. This helps in adapting the learning rate dynamically, allowing for faster convergence. ADAM is widely used due to its effectiveness and ease of implementation in various deep learning frameworks.