Adam Optimizer

The Adam Optimizer is an advanced optimization algorithm used in training machine learning models, particularly deep learning models. It combines the benefits of two other popular optimization techniques: AdaGrad and RMSProp. Adam adjusts the learning rate for each parameter individually, based on the first and second moments of the gradients, which helps in achieving faster convergence and better performance on complex datasets. It is widely used due to its efficiency and effectiveness in handling sparse gradients and non-stationary objectives, making it a go-to choice for practitioners in various machine learning applications.