Cross Attention vs Self Attention

CodeEmporium · Beginner ·🧠 Large Language Models ·3y ago

Key Takeaways

Cross-attention and self-attention mechanisms in deep learning for translation tasks, specifically English to Kannada, using query, key, and value vectors.

Full Transcript

what is cross-attention versus self-attention let's say we want to perform translation from English to a language called kannada that looks like this during attention each word is encoded as a query key and value Vector query vectors encode what am I looking for key Vector encodes what can I offer valuing codes what I actually offer during attention during self-attention each English word is converted into a query key and value vector and these are used to create vectors that better understand context during cross-attention every English word is converted into a key and value Vector as before but every Canada word is converted to a query Vector the final vectors will encode the countertop words taking into account the English words

Original Description

#deeplearning #machinelearning #chatgpt #neuralnetwork
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This video explains the difference between cross-attention and self-attention in deep learning, using the example of translating English to Kannada. It covers how each word is encoded as a query, key, and value vector, and how these vectors are used to create context-aware representations. By understanding these attention mechanisms, viewers can better appreciate how neural networks process and generate human language.

Key Takeaways
  1. Convert English words into query, key, and value vectors for self-attention
  2. Convert English words into key and value vectors, and Kannada words into query vectors for cross-attention
  3. Use these vectors to create context-aware representations of the input words
💡 Cross-attention allows the model to take into account the context of both the source and target languages, enabling more accurate translations.

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