Epoch Training

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Epoch training refers to how many times an AI model goes through the entire training dataset. One epoch means the model has seen every training example once. During an epoch, the data is usually processed in batches, and after each batch, the model updates its weights based on the errors it made. Because one pass isn’t enough for the model to learn meaningful patterns, training usually involves many epochs so the model can gradually reduce its loss and make better predictions.

Choosing the right number of epochs is especially important. Too few epochs and the model won’t learn enough (underfitting). Too many and it may memorize the training data instead of generalizing (overfitting). Tracking loss and metrics per epoch gives a clear picture of how learning is progressing and whether the model is improving consistently over time.

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