Monitoring and Logging
Machine Learning
An optimization technique that accelerates gradient descent by adding a fraction of the previous update to the current update.
Detailed Explanation
Momentum Optimization is a technique in machine learning that accelerates gradient descent by incorporating a fraction of the previous update into the current one. This helps in smoothing the updates, reducing oscillations, and speeding up convergence, especially in areas with ravines or noisy gradients. It effectively "builds momentum," allowing the model to maintain direction and improve training efficiency.
Use Cases
- •Speeds up training convergence in neural networks by smoothing updates and reducing oscillations during backpropagation.
Related Terms
Other terms in the Machine Learning category