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Self-Attention in Transformers: Computation logic and implementation
April 28, 2024, 5:34 a.m. | Anthony Demeusy
Towards AI - Medium pub.towardsai.net
Self-Attention in Transformers: Computation Logic and Implementation
Self-attention untangles the relationships between tokens in deep learningAttention serves as a fundamental concept for transformer architecture and for Large Language Models, playing a pivotal role in capturing dependencies between different words in a sequence. It intervenes in several building blocks of the Transformer architecture, more specifically, the multi-head self-attention, cross-attention, and masked attention stages.
Attention-based stages in the Transformer architecture, based on Attention is All You Need, Wasnani et al.arXiv:1706.03762 …
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