Momentum Optimization

Momentum optimization is a technique used in training machine learning models, particularly in deep learning. It helps accelerate the convergence of gradient descent by adding a fraction of the previous update to the current update, effectively smoothing out the optimization path. This method can help overcome issues such as oscillations and local minima, leading to faster training times and improved performance. Common use cases include training neural networks, where it helps in navigating the complex loss landscapes efficiently.