Translations:Attention Mechanisms/1/en - Revision history

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	'''Attention mechanisms''' are a family of techniques that allow neural networks to focus selectively on relevant parts of their input when producing each element of the output. Originally introduced to overcome the limitations of fixed-length context vectors in sequence-to-sequence models, attention has become the foundational building block of modern architectures such as the [[Transformer]].		'''Attention mechanisms''' are a family of techniques that allow neural networks to focus selectively on relevant parts of their input when producing each element of the output. Originally introduced to overcome the limitations of fixed-length context vectors in {{Term\|sequence-to-sequence}} models, attention has become the foundational building block of modern architectures such as the [[Transformer]].

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	'''Attention mechanisms''' are a family of techniques that allow neural networks to focus selectively on relevant parts of their input when producing each element of the output. Originally introduced to overcome the limitations of fixed-length context vectors in ~~{{Term\|~~sequence-to-sequence}} models, attention has become the foundational building block of modern architectures such as the [[Transformer]].		'''Attention mechanisms''' are a family of techniques that allow neural networks to focus selectively on relevant parts of their input when producing each element of the output. Originally introduced to overcome the limitations of fixed-length context vectors in sequence-to-sequence models, attention has become the foundational building block of modern architectures such as the [[Transformer]].

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	'''Attention mechanisms''' are a family of techniques that allow neural networks to focus selectively on relevant parts of their input when producing each element of the output. Originally introduced to overcome the limitations of fixed-length context vectors in sequence-to-sequence models, attention has become the foundational building block of modern architectures such as the [[Transformer]].		'''Attention mechanisms''' are a family of techniques that allow neural networks to focus selectively on relevant parts of their input when producing each element of the output. Originally introduced to overcome the limitations of fixed-length context vectors in {{Term\|sequence-to-sequence}} models, attention has become the foundational building block of modern architectures such as the [[Transformer]].

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'''Attention mechanisms''' are a family of techniques that allow neural networks to focus selectively on relevant parts of their input when producing each element of the output. Originally introduced to overcome the limitations of fixed-length context vectors in sequence-to-sequence models, attention has become the foundational building block of modern architectures such as the [[Transformer]].