Double Descent is a term used in machine learning that refers to a phenomenon where the performance of a model increases as the number of parameters increases, up to a certain point, after which the performance starts to degrade as the number of parameters continues to increase. This phenomenon contradicts the conventional wisdom that simpler models are better.

The concept of Double Descent was first introduced in a paper by Belkin et al. in 2018. The authors demonstrated that, for certain types of models, adding more parameters can actually improve the model's performance, even to the point of outperforming simpler models. However, if too many parameters are added, the model performance drops sharply.

Double Descent is counterintuitive because it suggests that adding more complexity to a model can actually be beneficial, up to a point. This phenomenon has important implications for the field of machine learning because it challenges the conventional wisdom that simpler models are always better.

To take advantage of Double Descent, it is important to carefully choose the number of parameters in a model. Too few parameters may result in underfitting, while too many parameters may result in overfitting. The optimal number of parameters may vary depending on the specific problem being tackled.

In conclusion, Double Descent is a phenomenon in machine learning where the performance of a model increases as the number of parameters increases, up to a certain point, after which the performance starts to degrade. This phenomenon challenges the conventional wisdom that simpler models are always better and has important implications for the field of machine learning.