The Adam optimizer, short for Adaptive Moment Estimation, is a widely used optimization algorithm in machine learning and deep learning. It combines the advantages of two other popular methods: AdaGrad and RMSProp. By adapting the learning rate for each parameter based on the first and second moments of the gradients, Adam provides efficient and effective optimization, particularly in complex models with numerous parameters. Its ability to handle sparse gradients and its robustness make it a favorite among practitioners, often leading to faster convergence during training. Understanding Adam’s mechanics can significantly enhance model performance in various applications.