🌉 ResNet (Residual Networks)

In 2015, a huge problem was discovered: If you make a CNN too deep (like 50 layers), it stops learning and gets worse!

🛣️ The Highway Analogy

Imagine driving through a city where you have to stop at every single traffic light (Layer). It's exhausting, and the signal degrades. ResNet added "Skip Connections" (highways). It allows the data to skip over certain layers entirely if those layers aren't being useful.

This allowed scientists to build networks with 152 layers that trained perfectly, revolutionizing deep learning forever!

🐍 Python Implementation

PyTorch's torchvision library contains pre-built, world-class ResNet models!

import torch
import torchvision.models as models

# Download an entire ResNet-18 model built by Microsoft!
resnet = models.resnet18()

# The model expects a 224x224 image
dummy_img = torch.randn(1, 3, 224, 224)
output = resnet(dummy_img)

# It outputs 1000 categories (from the ImageNet dataset)
print("Output shape:", output.shape)