What are epochs in deep learning?

An epoch is one complete pass through the entire training dataset during neural-network training.

If a dataset contains 10,000 training examples, then one epoch means the model has processed all 10,000 examples once.

Why are multiple epochs used?

A model usually does not learn enough from seeing the training data only once. Training therefore repeats over the dataset for multiple epochs.

Epoch 1 → model sees all training data once
Epoch 2 → model sees all training data again
Epoch 3 → another complete pass

With each epoch, the optimizer continues updating the model parameters based on the gradients computed during training.

Epochs and batches

Training data is usually split into smaller groups called batches rather than processed all at once.

For example:

Training examples = 1,000
Batch size = 100

Batches per epoch = 10

So one epoch contains 10 parameter-update steps in this example.

A dedicated answer, “What is batch size in deep learning?”, covers batches in more detail.

Can too many epochs be a problem?

Yes. Training for too few epochs can leave the model undertrained, while training for too many can contribute to overfitting.

Techniques such as early stopping can help decide when to stop training.

In short, an epoch is one full pass through the training dataset, and deep-learning models are usually trained for multiple epochs so their parameters can improve over repeated exposure to the data.