The video tutorial introduces residual networks, a form of deep convolutional neural networks with over 150 layers, and explains how they address the overfitting problem. It discusses the concept of overfitting, where training error decreases but validation error eventually increases, indicating a divergence. The tutorial then delves into the architecture of residual networks, focusing on the residual block, which includes a conventional neural network path and an identity map path. This dual-path approach helps mitigate overfitting by allowing direct information flow. The video concludes with the applications of residual networks in computer vision and other image tasks.

What new insight or understanding did you gain from this video?