Description: Epoch loss is a fundamental concept in the training of machine learning models and neural networks. It refers to the amount of error or discrepancy calculated at the end of each epoch, which is a complete iteration through the training dataset. During the training process, the model adjusts its parameters to minimize this loss, indicating how well it is learning to perform the assigned task. Loss can be measured using various functions, such as mean squared error for regression problems or cross-entropy for classification. Proper tracking of epoch loss allows researchers and developers to identify whether the model is improving, stagnating, or overfitting to the data. Additionally, visualizing loss over epochs can provide valuable insights into the dynamics of training, helping to adjust hyperparameters and make decisions about training duration. In summary, epoch loss is a key metric that guides the optimization process in machine learning, allowing for the evaluation of model performance and its ability to generalize to unseen data.
