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  "authorName": "Oliver Wyman",
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  "notes": "Explains the mechanism of adding positional encoding vectors to word embedding vectors to preserve sequence information.",
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      "text": "The resulting vectors represent both the meaning and position of tokens.",
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      "text": "First, obvious reason: the order of the tokens need to be considered. Solution: Positional Encoding (see below)\nSecond, less obvious reason: some words “care” more about each other than others. Solution: Self-Attention (see next slides)",
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      "text": "Network nodes need to consider multiple tokens at once. How to do that? A naïve approach of simply taking an average or a sum of all word embedding vectors would be wrong for two reasons.",
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      "text": "Token | Word Embedding | Positional Encoding | Resulting vectors\nName | (0.638...) | + (0, 1, 0, 1, ...) | = (0.638, 1.759...)\nthe | (0.655...) | + (0.031, 1.000...) | = (0.686, 1.324...)\ncapital | (0.082...) | + (0.062, 0.998...) | = (0.144, 1.324...)\nof | (0.194...) | + (0.094, 0.995...) | = (0.288, 1.289...)\nPeru | (0.825...) | + (0.125, 0.992...) | = (0.95, 1.935...)",
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      "text": "REPRESENTING ORDER OF WORDS WITH POSITIONAL ENCODING",
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