Model Convergence

Topic

Model convergence is a state in machine learning and mathematical optimization where an iterative training algorithm stabilizes and ceases to significantly change its parameters or loss value. It indicates that the model has successfully found a local or global minimum of the loss function, meaning further training iterations will yield negligible improvements in performance. Achieving convergence is a critical milestone in the training process, often monitored using validation metrics to prevent overfitting.