what is an epoch in machine learning

In machine learning, an epoch refers to one complete pass of the 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 training data is always broken down into small batches to overcome the issue that could arise due to storage space limitations of a computer system. These smaller batches can be easily fed into the machine learning model to train it. This process of breaking it down to smaller bits is called batch in machine learning. This procedure is known as an epoch when all the batches are fed into the model to train at once. The number of epochs is considered a hyperparameter that defines the number of times the entire dataset has to be worked through the learning algorithm. 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.