Nesterov is a variant of the momentum optimizer.

Nesterov Accelerated Gradient (NAG) is indeed a variant of the momentum optimizer. In the traditional momentum method, the gradient is used to adjust the direction based on the current momentum. Nesterov, on the other hand, anticipates the change in the momentum by calculating the gradient at a slightly altered position. This small adjustment leads to better convergence and more efficient optimization, especially in non-convex problems.

Momentum methods and their variants like Nesterov are commonly discussed in the optimization strategies for neural networks, including frameworks such as TensorFlow, which is covered in Huawei's HCIA AI courses.

HCIA AI References:

Deep Learning Overview: Discussion of optimization algorithms, including gradient descent variants like Momentum and Nesterov.

AI Development Framework: Explains the use of Nesterov in deep learning frameworks such as TensorFlow and PyTorch.