Эпоха — это один полный проход по всему обучающему набору данных во время обучения модели. Число эпох определяет, сколько раз модель видит каждый обучающий пример, и должно выбираться аккуратно, чтобы сбалансировать прогресс обучения и переобучение.
In machine learning, particularly in the creation of artificial neural networks, an epoch is training the model for one cycle through the full training dataset. Small models are typically trained for as many epochs as it takes to reach the best performance on the validation dataset. The largest models may train for only one epoch.
A full training pass over the entire training set such that each example has been processed once. An epoch represents /batch size training iterations, where is the total number of examples. For instance, suppose the following: - The dataset consists of 1,000 examples. - The batch size is 50 examples. Therefore, a single epoch requires 20 iterations: See Linear regression: Hyperparameters in Machine Learning Crash Course for more information.