Deep Learning 10 : What is an Epoch?

 

🔄 What is an Epoch?

• An epoch is one complete pass through the entire training dataset by the model.

• If you have 1,000 samples and a batch size of 100:

  • One epoch = 10 batches (because 100 × 10 = 1,000).

• After each epoch, the model has seen all training data once.

⚙️ Why Multiple Epochs?

• A single epoch usually isn’t enough for the model to learn meaningful patterns.

• Training for multiple epochs allows the model to gradually adjust weights and improve accuracy.

• Too few epochs → underfitting (model hasn’t learned enough).

• Too many epochs → overfitting (model memorizes training data, performs poorly on unseen data).

📌 Epochs vs. Batches vs. Iterations

Term Meaning:

Batch	Subset of the dataset processed at once (e.g., 32 samples).
Iteration	One update step of weights (processing a single batch).
Epoch	One full pass through the dataset (all batches processed once).

So:

• Epochs = how many times the model sees the full dataset.

• Iterations = how many times weights are updated.

• Batches = how many samples are processed per iteration.

✅ Example

• Dataset size = 10,000 samples

• Batch size = 100

• Epochs = 5

➡️ Each epoch = 100 iterations (10,000 ÷ 100). ➡️ Total training = 500 iterations (100 × 5).

In short: Epochs are the number of times the model cycles through the entire dataset during training.