normalization

LayerNorm sits in every transformer, and implementing it checks that you know what it normalizes over and why. The signal is normalizing across the feature dimension per token, the learnable scale/shift, and the RMSNorm simplification modern LLMs adopted.

Implementing BatchNorm checks that you know the train/inference difference, the part everyone forgets. The signal is normalizing over the batch with running stats maintained for inference. Here is the implementation.

The textbook variance formula loses catastrophic precision on streaming data and cannot update incrementally. Welford's one-pass update is the standard for online feature normalization. Here it is.

BatchNorm is one of the most-asked deep-learning questions, and the trap is the train/inference difference. The signal is what it normalizes, why it stabilizes and speeds training, and why it switches to running statistics at inference. Here is that answer.

Preprocessing quietly decides whether a model trains well, and the right choice is model-dependent. The signal is knowing when scaling matters versus when it is wasted effort, and how to encode a feature with thousands of categories without blowing up your matrix.

A data-warehouse fundamentals question that separates people who model for analytics from people who only know OLTP normalization. The signal is facts vs dimensions, the star schema, and why analytics denormalizes where transactional systems normalize.