Attention? Attention!
Read OriginalThis technical article provides an in-depth explanation of the attention mechanism in neural networks. It starts by drawing an analogy to human visual attention, then details how attention works as a vector of importance weights in deep learning models. The article critiques the limitations of traditional seq2seq models and introduces the encoder-decoder architecture, setting the stage for advanced models like the Transformer, Pointer Networks, and Neural Turing Machines, with links to implementations.
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