What is Attention Heads?
Parallel attention mechanisms within a transformer that each focus on different types of relationships in the input.
Why It Matters
Multiple attention heads let AI models capture different types of patterns simultaneously, improving understanding.
Real-World Example
One attention head might focus on grammar while another tracks which pronouns refer to which nouns.
“Understanding terms like Attention Heads matters because it helps you have better conversations with developers and make smarter decisions about your software. You do not need to be technical. You just need to know enough to ask the right questions.”
Related Terms
Attention Mechanism
A technique that lets AI models focus on the most relevant parts of the input when generating output.
Self-attention
A mechanism where each word in a text considers its relationship to every other word in the same text.
Transformer
A type of AI architecture that processes text by paying attention to relationships between all words at once, rather than reading sequentially.
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Related Terms
Transformer
A type of AI architecture that processes text by paying attention to relationships between all words at once, rather...
Attention Mechanism
A technique that lets AI models focus on the most relevant parts of the input when generating output.
Self-attention
A mechanism where each word in a text considers its relationship to every other word in the same text.
Large Language Model (LLM)
An AI system trained on massive amounts of text that can understand and generate human language.
Tokenisation
The process of breaking text into smaller pieces called tokens that an AI model can process.
Embeddings
A way of representing words, sentences, or other data as lists of numbers that capture their meaning.