Neura saysNormalization 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.
Power-ups you unlock
Keep activations well-scaled across layers
Batch norm: across a batch (CNNs)
Layer norm: across features (transformers)
Stabilizes training; allows deeper nets
The Overfit Ogre attacks — common mistakes
Batch norm with tiny/variable batch sizes
Confusing batch vs layer norm axes
Forgetting norm placement matters
Boss battleExplain why transformers prefer layer norm over batch norm.
Example code
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<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>