Convergence

In machine learning, convergence refers to the point during training when a model's performance on the training data stops significantly improving with further iterations or adjustments. It indicates that the optimization algorithm, such as gradient descent, has found a stable solution or a local minimum for the loss function. Achieving convergence means the model has learned as much as it can from the current data and architecture.