KimAnt ๐Ÿฅฆ

KimAnt ๐Ÿฅฆ

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KimAnt ๐Ÿฅฆ

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  • [GoogleML] Recurrent Neural Networks

    Why Sequence Models? Notation ๋‹จ์–ด ๋‹จ์œ„๋กœ ๋Š์€ ๋’ค, ์ด๋ฆ„์— ๊ด€๋ จํ•œ ๋ถ€๋ถ„์„ ์‹๋ณ„ํ•œ๋‹ค๊ณ  ํ•˜์ž (์ด๋ฆ„ y -> 1, ์•„๋‹ˆ๋ฉด 0) X -> ์ฒซ๋ฒˆ์งธ ๋‹จ์–ด i ๋ฒˆ์งธ sample(๋ฌธ์žฅ)์— ๋Œ€ํ•ด t๋ฒˆ์งธ ๋‹จ์–ด, ์š”์†Œ -> X(i) ๋ผ๊ณ  ํ‘œ๊ธฐํ•œ๋‹ค Tx(i) = 9 (๋‹จ์–ด ๊ฐœ์ˆ˜๊ฐ€ 9๊ฐœ๋ผ๋Š” ์˜๋ฏธ) one-hot vector๋กœ ํ‘œํ˜„๋˜๋Š” ๊ฐ ๋‹จ์–ด๋“ค ex) X ์ฃผ์–ด์ง„ ์‚ฌ์ „, vocavulary์— ๋Œ€ํ•ด, mapping ๋˜๋Š” ๊ฐ’๋งŒ 1, ๋‚˜๋จธ์ง€๋Š” 0 -> one-hot ๋งŒ์•ฝ์— ์‚ฌ์ „์— ์—†๋Š” ๋‹จ์–ด๊ฐ€ ์ฃผ์–ด์ง„๋‹ค -> unknown ์ด๋ฅผ ํ†ตํ•ด x -> y mapping Recurrent Neural Network Model ๊ธฐ์กด์˜ ๊ตฌ์กฐ๋กœ๋Š” ํ‘œํ˜„ํ•˜๊ธฐ ์–ด๋ น๋ˆ„ sequential data ๊ฐ€์ค‘์น˜๋“ค์„ ๊ณต์œ ํ•œ๋‹ค! y3์„ ์˜ˆ์ธกํ•˜๊ธฐ..

    2023.10.17
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