Double descent

Double descent is a phenomenon where the performance of a machine learning model first decreases, then increases as the model is given more training data to learn from. This is different from the usual pattern of performance, where it gets better with more data. With double descent, a machine learning model can become more accurate even after it has been trained on more data than it needs. This can be helpful for machine learning applications because it can make them more reliable when presented with more data than expected.