Understanding Transformers
The discussion delves into the structure of transformers, contrasting the traditional left-to-right sequence of encoders and decoders with the parallel processing capabilities of transformers. As words are input into the model, they are transformed into vectors with semantic meaning, enabling neural networks to process language effectively. The analogy of a five-story building helps visualize the multi-level architecture of the decoder and the role of the encoder in this innovative model.In this clip
From this podcast
Super Data Science: ML & AI Podcast with Jon Krohn
759: Full Encoder-Decoder Transformers Fully Explained — with Kirill Eremenko
Related Questions
How does this language model work?
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?
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?