How deeply does Deep Learning with R, Third Edition cover attention score?

Explains dot products, softmax normalization, weighted sums, batched score matrices, and their role in attention computation.

Attention score

As explained in Deep Learning with R, Third Edition

An attention score is a numerical value measuring the relevance of one vector or sequence position to another in an attention calculation.

An attention score measures how relevant a source vector is to the target vector currently being processed. In sequence-to-sequence translation, high-scoring source tokens contribute more information to the prediction, helping the model retrieve relevant context without passing it through a long recurrent loop. section 15.3.1

Dot-product attention turns scores into a context representation:

Compute the dot product between each target vector and each source vector; nearby vectors receive higher scores. section 15.3.1
Apply softmax across the source positions so the scores for each target sum to 1. section 15.3.1
Take the weighted sum of source vectors, using the normalized scores as weights. section 15.3.1
For target and source sequences, organize the results as a matrix whose rows describe one target position’s source relevance. section 15.3.1

Minimal attention calculation

For one target vector, score every source vector, normalize the scores, and combine the sources: scores <- sources |> sapply(\(source) score(target, source)) |> softmax(); combined <- sum(scores * sources).

How Deep Learning with R, Third Edition covers it

Cover of Deep Learning with R, Third Edition

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Also explained in

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Graph Neural Networks in ActionAn unnormalized learned score quantifying the importance of one connected node's features to another node.
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Build a Text-to-Image Generator (from Scratch)An attention score is a numerical measure of the relevance between a query and a key.
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The AI Pocket BookA numerical value used by an attention mechanism to weight the relevance of one element relative to others.

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Related concepts

inner productproduced by · An inner product is a scalar-valued operation on two vectors that combines their corresponding components, summing their products and, for complex vectors, using conjugation.
attention weightproduces · An attention weight is a computed coefficient, often normalized, that determines how strongly an input element or value contributes to an attention-based representation.
queryproduced by · An attention query is a vector representation of the information sought by an input position, compared with key representations to determine which information to attend to.
attentionproduced by · Attention is a neural-network mechanism that computes input-dependent weights to combine information according to its relevance to a computation.
keyproduced by · In attention, a key is a vector representation of an input element that is compared with a query to determine relevance and attention weights for its associated value.
softmaxuses · Softmax is a function that exponentiates a vector of scores and normalizes the results into nonnegative values that sum to one.

This explainer was generated by Manning's knowledge graph from the text of Deep Learning with R, Third Edition. The section links point to the parts of the book it draws on.