The Adam Optimizer, short for Adaptive Moment Estimation, is a widely used optimization algorithm in machine learning, particularly in training deep learning models. It combines the benefits of two other popular algorithms: RMSProp and Stochastic Gradient Descent (SGD) with momentum. Adam dynamically adjusts the learning rates for each parameter based on the first and second moments of the gradients, allowing for efficient convergence even in complex landscapes. This adaptability makes it a preferred choice among practitioners, as it often leads to faster training times and improved performance in various machine learning tasks.