Deep neural nets have a natural tendency to overfit. Ideally, you have a held-out test which the model hasn't seen and only used after model has trained and tu

Deep neural nets have a natural tendency to overfit. Ideally, you have a held-out test which the model hasn't seen and only used after model has trained and tuned on the dev and validation sets. Often bad experimental models will repeatedly use the test set in fine-tuning an existing model which may result in your model learning about the test set rendering the test set useless. In the practice real world results may vary as your training and test data may not represent the actual distribution of the real world data.