Network Simulations
Machine Learning
A momentum-based optimization method that provides a predictive update to the parameters before computing the gradient.
Detailed Explanation
Nesterov Accelerated Gradient (NAG) is an optimization technique that improves upon traditional momentum methods by calculating the gradient after applying a tentative momentum step. This predictive approach allows for more accurate and efficient updates of model parameters, leading to faster convergence and better performance in training neural networks, especially in complex loss landscapes.
Use Cases
- •Accelerate neural network training convergence by predicting parameter updates, reducing iteration time and enhancing model accuracy through proactive gradient adjustments.
Related Terms
Other terms in the Machine Learning category