Description: Epochs in the context of machine learning refer to the number of complete iterations a model performs over the training dataset. Each epoch means the model goes through the entire dataset once, adjusting its internal parameters to minimize the loss function. Convergence occurs when the model reaches a state where changes in the loss function become minimal, indicating that it has effectively learned from the data. The choice of the number of epochs is crucial, as an insufficient number can lead to an underfitted model, while an excessive number can result in overfitting, where the model adapts too closely to the training data and loses generalization capability. Therefore, it is common to use techniques such as cross-validation or monitoring the loss function on a validation set to determine the optimal moment to stop training. In summary, epochs are a fundamental component in the training process of models in machine learning, directly influencing their performance and generalization ability.
