Momentum optimizers are advanced techniques used in gradient-based optimization to enhance the convergence speed of machine learning models. By incorporating a momentum term, these optimizers help accelerate learning in directions with low curvature while dampening oscillations in high curvature areas. This approach allows for smoother and faster convergence, making it particularly effective in training deep learning models. Commonly used momentum values, such as 0.9, are often set by default in many frameworks, but recent research suggests that dynamically adjusting momentum can further improve optimization performance. Understanding momentum optimizers is crucial for developing efficient machine learning algorithms.