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Vocabulary

Understand the term. Connect the concept.

A practical dictionary of 512 AI, machine learning, mathematics, and programming terms from HerbDev’s AI Vocabulary app. Start with a definition, explore an example, then follow the concepts behind it.

Linked concepts open in a new tab. Audio reads the definition and example.

Weight

Neural Networks · Level 2 of 5

Weight

A learned coefficient controlling an input's contribution to a computation.

Changing a weight changes how strongly a signal affects the output.

Example

A coefficient multiplies a feature before it enters a weighted sum.

Listen to the definition and example

Audio transcript

Weight. A learned coefficient controlling an input's contribution to a computation. Changing a weight changes how strongly a signal affects the output. For example: A coefficient multiplies a feature before it enters a weighted sum.

Explore this concept

A useful analogy

A volume control for one contribution entering a mixture.

Why it matters

This is part of the vocabulary used to read network diagrams and understand parameter updates.

Technical detail

For an affine unit z = Σ_i w_i x_i + b, w_i scales input x_i.

Common misconception

A large raw weight does not imply importance independently of feature scale and correlations.

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Related concepts

Quick recall question

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Put the terminology into context: How AI works · Machine-native representations · All learning paths

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