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Nesterov Accelerated Gradient NAG Optimizer
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The Nesterov Accelerated Gradient (NAG) Optimizer is an advanced optimization algorithm used in machine learning to speed up gradient descent. It's an enhancement of the standard momentum method, distinguished by its ability to "look ahead" by adjusting its gradients based on a future predicted position. This foresight is achieved by incorporating a fraction of the previous update step into the current gradient computation. By doing so, NAG reduces overshooting and oscillations during optimization, leading to faster convergence towards the minimum of a loss function. It's particularly effective for training deep neural networks, where the landscape of the loss function can be complex.
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