What are training epochs in neural network training?

Training epochs refer to the number of complete passes through the entire training dataset during the training process of a neural network. Each epoch involves using the entire dataset to update the model's weights based on the errors made in the previous predictions. This iterative approach allows the model to learn from the data, gradually improving its performance over time.

When a model goes through multiple epochs, it processes the data repeatedly, which helps it to refine its understanding and adjust its parameters accordingly. This is crucial for effectively training the model, as it enables better convergence towards an optimal solution. The more epochs the model goes through, the more opportunities it has to learn from the data, though there is a point of diminishing returns where the model may begin to overfit the training data if trained too long.