In machine learning, one entire transit of the training data through the algorithm is known as an epoch. The epoch number is a critical hyperparameter for the algorithm. It specifies the number of epochs or full passes of the entire training dataset through the algorithm’s training or learning process. The internal model parameters of the dataset are updated with each epoch.
As a result, the gradient learning algorithm is named after a single batch epoch. An epoch’s batch size is typically one and is always an integer value.
It can alternatively be represented as an epoch-numbered for-loop, with each loop path traversing the complete training dataset.
When using training algorithms, the number of epochs can reach thousands, and the process is programmed to continue until the model error is suitably minimized. Typically, tutorials and examples employ figures such as 10, 100, 1000, or even higher.
For the training process, line plots may be made using machine learning epochs on the X-axis and the skill or model error on the Y-axis. These line graphs are referred to as the algorithm’s learning curve, and they can be used to diagnose issues such as the training set being underfitting, overfitting, or appropriately learned.
When a certain amount of samples are processed, the model is updated. This is referred to as the sample batch size. The number of complete passes in the training dataset is equally significant and is referred to as the epoch in machine learning.
As a result, the method can be performed for any length of time. A set epoch number and the factor of the rate being zero over time can be used to stop the algorithm from running.
Both batch size and epoch are hyper-parameters in machine learning of learning algorithms, with integer values used by the training model. These values are not discovered by a learning process because they are not internal model parameters and must be specified for the process when training an algorithm on the training dataset.