Nadam, short for Nesterov-accelerated Adaptive Moment Estimation, is an advanced optimization algorithm used in training deep learning models. It combines the benefits of the Adam optimizer with Nesterov momentum, which allows for more responsive updates to model parameters. By incorporating Nesterov momentum, Nadam improves the convergence speed and stability of the optimization process, particularly in complex landscapes. This makes it particularly effective for tasks involving large datasets and deep neural networks. Nadam is widely adopted in various machine learning frameworks, including PyTorch, due to its ability to handle adaptive learning rates efficiently.