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Concept Glossary: W

This page lists English glossary entries for this letter. Entry bodies are generated from per-term source files with includes.

weight

  • Meaning: A weight is a parameter that controls how strongly an input value or intermediate value affects a model's output calculation. It is a numerical handle for connection strength or influence.
  • Why it matters: In many machine-learning and deep-learning models, training can be read as adjusting weights so the input-output relationship fits the task better. The concept makes "the model learns" concrete as changing numerical parameters in the calculation.
  • Related concepts: parameter, representation, model training
  • Core Section: P1-4.3
  • Appears in: P1-5.1, P1-12.1, P2-11.1, P6-21.1

weighted sum

  • Meaning: A weighted sum multiplies each input by its own weight and then adds the results into one value. For example, \(x_1w_1 + x_2w_2\) folds two inputs into one score while reflecting them with different strengths.
  • Why it matters: Many AI model calculations, including linear layers, matrix multiplication, and attention scores, repeat this pattern. The term keeps the role of the weight clear: a weight is not just a number being multiplied, but a value that controls how strongly an input is reflected.
  • Related concepts: weight, linear combination, matrix multiplication, activation function
  • Core Section: P2-3.3
  • Appears in: P5-1.1, P5-1.2