NAdam is an optimization algorithm that combines the benefits of Adam and Nesterov momentum. It is designed to improve the convergence speed and performance of training deep learning models. The key parameters for configuring NAdam include:

1. params: This is an iterable of parameters to optimize or dictionaries defining parameter groups.
2. lr (learning rate): The default value is set to 2e-3, which controls the step size during optimization.
3. betas: A tuple of coefficients used for computing running averages of gradients and their squares, with a default of (0.9, 0.999).
4. eps: A small term added to the denominator for numerical stability, defaulting to 1e-8.
5. weight_decay: This is the L2 penalty for weight decay, with a default of 0.
6. momentum_decay: This parameter controls the momentum decay, with a default value of 4e-3.

NAdam can also accommodate additional configurations, such as using a foreach implementation of the optimizer and adding parameter groups for fine-tuning pre-trained networks ¹.