Revolutionizing Deep Learning with ResNet

Imagine a world where every extra layer of intelligence you add to a machine doesn’t make it

smarter—but actually makes it worse. Imagine pouring hours, days,

even weeks of training into a deep neural network, only to watch it perform worse than

a simpler one. It sounds impossible, right? Like adding more gears to a machine

that should run smoother, only for it to break down faster. But that was the

strange reality haunting computer vision back in the mid-2010s. Researchers were

pushing deeper and deeper architectures, chasing better accuracy and richer representations, and suddenly… nothing

worked. The deeper networks weren’t learning more—they were failing harder.