Deep learning expert Lilian Weng explores the idea that deep neural networks may not be as prone to overfitting as one might expect, given their large number of parameters and perfect training performance. She discusses the concept of overfitting and how it relates to deep learning models, citing research by Frankle and Carbin (2019) on the lottery ticket hypothesis. The article suggests that overfitting may not be as significant a concern for deep neural networks as previously thought. --- Why it matters: Understanding the extent to which deep neural networks overfit is crucial for researchers seeking to improve their generalizability and prevent poor performance on out-of-sample data. Source: https://lilianweng.github.io/posts/2019-03-14-overfit/