In the realm of machine learning, an epoch represents one complete iteration through the entire dataset during the training process. For instance, if a dataset contains 1000 samples and a model is trained with a batch size of 100, it will require 10 epochs to cycle through the entire dataset once.
During each epoch, the model processes a batch of data. It makes predictions based on its existing set of weights and biases. Subsequently, the model compares these predictions with the actual labels and computes the error. This error is then utilized to update the model's weights and biases, aiming to enhance its performance.
The number of training epochs significantly influences a model's performance. If a model is trained for too few epochs, it might not have sufficient time to learn, resulting in subpar performance. Conversely, if a model undergoes an excessive number of training epochs, it may start to overfit the training data, causing it to perform poorly on unseen data.
Consequently, determining the optimal number of epochs for a specific model and dataset is a vital step in machine learning. This can be achieved through methods such as early stopping. Early stopping entails halting the training process as soon as the model's performance on a validation set starts to decline.