What is epochs definition?

In the context of machine learning and deep learning, an epoch refers to a complete iteration of training the entire dataset in the neural network model. In other words, an epoch is one full cycle in which the model has seen, processed, and learned from all the training examples available in the dataset.

During each epoch, the model updates its internal parameters based on the gradients of the loss function. By iterating through multiple epochs, the model gets the opportunity to improve its predictions and learn more complex patterns from the data.

The number of epochs required for optimal model performance can vary depending on factors such as the complexity of the dataset and the complexity of the neural network architecture. Choosing the optimal number of epochs might require some trial and error and experimentation with different values. Overfitting and underfitting are common issues that can occur when the number of epochs is not appropriately chosen, and this can affect the model's generalization ability and accuracy.