NAdam, short for Nesterov-accelerated Adaptive Moment Estimation, is an advanced optimization algorithm used in training machine learning models. It combines the benefits of the Adam optimizer with Nesterov momentum, which helps in accelerating convergence and improving performance. NAdam adjusts the learning rate dynamically based on the first and second moments of the gradients, allowing for efficient parameter updates. This optimizer is particularly useful in deep learning applications, where it can help navigate complex loss landscapes more effectively. By incorporating Nesterov momentum, NAdam provides a more responsive approach to optimization, enhancing the training process of neural networks.