Home▸Machine Learning & Neural Networks▸ResNet: How Residual Connections Made Deep Networks Trainable

🔀 Residual Connections: Why Deep Networks Need Shortcuts

Discover how residual connections solve the degradation problem in deep neural networks, letting gradients skip layers so networks with 100+ layers can actually train.

Machine Learning & Neural Networks3DModerate60 FPS

The simulation renders a stack of network layers in 3D, showing data flowing forward through weight transformations while highlighting how skip connections carry a parallel gradient path backward during training.

🔬 What It Demonstrates

🎮 How to Use

Choose the network depth and toggle residual connections on or off, then press play to watch forward activations and backward gradients animate through the stack, using the speed slider to slow down the flow.

💡 Did You Know?

The original ResNet paper trained a 1,202-layer network as an experiment — it trained successfully thanks to residual connections, though it slightly underperformed the 110-layer version due to overfitting on a small dataset, showing that trainability and generalization are separate challenges.

⚙ Under the hood

See how skip connections let signal and gradient flow unimpeded through very deep networks, solving the degradation problem that made deep nets untrainable.

resnetdeep learningresidual connectionsskip connectionsmachine-learningneural networks

3D · Three.js / WebGL renderer · 60 FPS target · runs fully client-side, no install