Adam

Adam (short for Adaptive Moment Estimation) is an algorithm used to help machine learning models learn efficiently. It adjusts how much a model updates based on the history of its past updates, allowing for faster and more reliable training. Think of it like a smart coach that learns from previous steps to give better guidance, balancing new information with past experience. This adaptiveness helps the model converge quickly to accurate results, especially when dealing with complex data or large networks. Overall, Adam makes the training process more efficient and stable.