Transformer Architecture Insights
Kirill explains the intricate process of transforming six vectors into context-rich outputs, emphasizing the parallel nature of the transformer architecture. Jon raises an intriguing point about the implications of language models like GPT-4 and their vast dictionaries, questioning how this translates when dealing with outputs beyond text, such as images. The conversation highlights the complexity and adaptability of modern AI systems.In this clip
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759: Full Encoder-Decoder Transformers Fully Explained — with Kirill Eremenko
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How do vector embeddings work in the context of the episode 747: Technical Intro to Transformers and LLMs — with Kirill Eremenko and the clip Understanding Q, K, V Vectors?
How do vector embeddings work in the context of the episode 747: Technical Intro to Transformers and LLMs — with Kirill Eremenko and the clip Word Embeddings Explained?