Arquitectura Transformer
Arquitectura de red neuronal basada en la autoatención.
Unlike earlier models (RNNs), Transformers process entire sequences of data simultaneously. This 'self-attention' allows the model to understand the relationship between distant words in a sentence, regardless of their position.
graph LR
Center["Arquitectura Transformer"]:::main
Rel_attention_mechanism["attention-mechanism"]:::related -.-> Center
click Rel_attention_mechanism "/terms/attention-mechanism"
Rel_transformer["transformer"]:::related -.-> Center
click Rel_transformer "/terms/transformer"
Rel_natural_language_processing["natural-language-processing"]:::related -.-> Center
click Rel_natural_language_processing "/terms/natural-language-processing"
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🧠 Prueba de conocimiento
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🧒 Explícalo como si tuviera 5 años
A revolutionary way for computers to read. Instead of reading word by word, it looks at the whole page at once to understand how every word relates to the others.
🤓 Expert Deep Dive
Introduced the multi-head attention mechanism. It eliminates recursion, allowing for massive parallelization during training. It is the backbone of BERT, GPT, and T5.