Nesterov Accelerated Gradient

Nesterov Accelerated Gradient (NAG) is an optimization technique used in machine learning and deep learning to improve the convergence speed of gradient descent algorithms. It modifies the traditional gradient descent approach by incorporating a momentum term that accounts for the past gradients, allowing for a more informed update of the model parameters. NAG anticipates the future position of the parameters, which helps in navigating the optimization landscape more efficiently. This technique is particularly useful in training deep neural networks, where it can lead to faster convergence and improved performance in tasks such as image classification and natural language processing.