Nadam, short for Nesterov-accelerated Adaptive Moment Estimation, is an advanced optimization algorithm that combines the benefits of Adam and Nesterov momentum. It is designed to improve the convergence speed and performance of machine learning models during training. By incorporating Nesterov momentum, Nadam provides a more responsive approach to gradient descent, allowing for better handling of the optimization landscape. This algorithm is particularly useful in scenarios where traditional methods may struggle, as it adapts the learning rate based on the first and second moments of the gradients. Nadam is widely used in deep learning frameworks for its efficiency and effectiveness.