In machine learning, an epoch refers to one complete pass of the entire training dataset through the algorithm. During an epoch, every training sample in the dataset is processed by the model, and its weights and biases are updated in accordance with the computed loss or error. The number of epochs is a hyperparameter that defines the number of times the entire dataset has to be worked through the learning algorithm. The number of epochs is considered a hyperparameter, and it is set by the user. The batch size is also a hyperparameter, and it is set by the user. The number of iterations per epoch can be found by dividing the total number of training samples by the individual batch size. An epoch can be comprised of one or more batches. The batch gradient descent learning algorithm is used to describe an epoch that only contains one batch. The number of epochs is traditionally large, often hundreds or thousands, allowing the learning algorithm to run until the error from the model has been sufficiently minimized. The number of epochs can be set to an integer value between one and infinity. Epochs allow you to train a model for longer, which may result in improved performance. Epochs also allow you to train a model on a larger dataset even if it doesn’t fit all at once in memory. Finally, epochs make early stopping simple, which is a useful technique for avoiding overfitting.