In short
A technique that normalizes layer inputs during training, stabilizing and speeding up convergence in deep networks.
How to learn this
- 1Train a deep network with and without batch norm and compare training stability.
- 2Learn where batch norm layers are typically placed relative to activations.
- 3Understand the difference in batch norm behavior between training and inference mode.
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