Batch Normalization: Normalizing Across the Batch
Batch Normalization standardizes each channel using the current batch's mean and variance while training, then switches to a memorized running average at inference. This is the layer that let CNNs go deeper and learning rates go 10x higher — and where a huge share of training bugs live.
01.The Problem: The Ground Keeps Shifting Under Your Layers
A deep network is a stack of layers. Each layer feeds the next.
Now here is the trouble. Training updates every layer's weights every step.
So the numbers one layer receives from the layer below are constantly changing. One step the incoming activations hover around 0. Next step they drift to 50. A step later they are tiny again.
"I just learned to handle inputs near 0 — now they are near 50!"
Each layer is chasing a moving target. Worse, some activations land in the flat tails of sigmoid or ReLU, where gradients go near zero and learning stalls.
So the question becomes:
Can we pin each layer's inputs to a stable, friendly range — mean around 0, spread around 1 — so training stops wobbling?
That is exactly what Batch Normalization (BN) does. It is one line of math, but it is the line that made very deep CNNs trainable.
BN Train vs Inference Paths
BN Train vs Inference Paths
Training normalizes by current-batch statistics (differentiable through them); inference swaps in running means/variances — the dual-path design that defines BN.
Unlock Topic #91: Batch Normalization: Normalizing Across the Batch
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