Nag as optimiser in deep learning – day 36

Nesterov Accelerated Gradient (NAG): A Comprehensive Overview Nesterov Accelerated Gradient (NAG): A Comprehensive Overview Introduction to Nesterov Accelerated Gradient Nesterov Accelerated Gradient (NAG), also known as Nesterov Momentum, is an advanced optimization technique introduced by Yurii Nesterov in the early 1980s. It is an enhancement of the traditional momentum-based optimization used in gradient descent, designed to accelerate the convergence rate of the optimization process, particularly in the context of deep learning and complex optimization problems. How NAG Works The core idea behind NAG is the introduction of a “look-ahead” step before calculating the gradient, which allows for a more accurate and responsive update of parameters. In traditional momentum methods, the gradient is computed at the current position of the parameters, which might lead to less efficient convergence if the trajectory is not perfectly aligned with the optimal path. NAG, however, calculates the gradient at a position slightly ahead, based on the accumulated momentum, thus allowing the algorithm to “correct” its course more effectively if it is heading towards a suboptimal direction. The NAG update rule can be summarized as follows: Look-ahead Step: Compute a preliminary update based on the momentum. Gradient Calculation: Evaluate the gradient at this look-ahead position. Momentum…