Adam Optimizer

Adam Optimizer is an algorithm used in training machine learning models that helps improve their accuracy efficiently. It adjusts the model's internal settings (parameters) by combining two methods: one that considers recent updates (momentum) and another that adapts learning rates for each parameter individually. This combination enables faster convergence to the best solution while maintaining stability, especially with complex or large datasets. Essentially, Adam intelligently navigates the learning process, making small, well-informed adjustments to improve the model’s performance over time.