AI Batch Norm Tutorial: Layer Norm, Group Norm

TL;DRNormalization layers (batch, layer norm) stabilize and speed up training.

Deep nets train better when activations stay well-scaled. Batch norm normalizes across a batch (great for CNNs, but batch-dependent). Layer norm normalizes across features within one example (batch-independent — the standard in transformers). They reduce sensitivity to initialization and learning rate, letting you train deeper, faster. Layer norm is why transformers train stably at scale.

Key points

Keep activations well-scaled across layers
Batch norm: across a batch (CNNs)
Layer norm: across features (transformers)
Stabilizes training; allows deeper nets

Common mistakes

Batch norm with tiny/variable batch sizes
Confusing batch vs layer norm axes
Forgetting norm placement matters

Try it: Explain why transformers prefer layer norm over batch norm.

Example code

<!doctype html><html><head><meta charset="utf-8"></head>
<body style="background:#06040d;color:#e6e0ff;font-family:monospace;padding:20px"><pre>batch norm: normalize across the batch (CNNs)
layer norm: normalize across features (one example)
→ transformers use layer norm (batch-independent)</pre></body></html>

Open the interactive lesson →

← Regularization · L1 · L2 · Dropout Overfitting · The Bias-Variance Trade →