Transformers and Attention: The Core Idea Without the Buzzwords
Attention lets a model compute relationships between positions in a sequence instead of processing every position as an isolated step.
2026-09-19
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Attention lets a model compute relationships between positions in a sequence instead of processing every position as an isolated step.
A model can look excellent because you accidentally gave it information about the evaluation set. Train/validation/test discipline is how you separate learning from measurement.
A model can become better at the training set while becoming worse at unseen data. That gap is the core of overfitting.
A strong feature is useful only if the same information would genuinely be available when the model has to make its prediction.
A model can improve one metric while becoming worse for the actual product. Good evaluation starts with the use case and the failure modes.