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๋ชฉ๋กRNN ์•Œ๊ณ ๋ฆฌ์ฆ˜ (1)

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[Deep Learning] RNN ์•Œ๊ณ ๋ฆฌ์ฆ˜ ๊ฐœ๋… ์ดํ•ด

๐Ÿ“š ๋ชฉ์ฐจ 1. RNN ๊ฐœ๋… 2. RNN ์ˆ˜์‹ 3. RNN ์šฉ๋„ 1. RNN ๊ฐœ๋… Recurrent Neural Network(RNN)์€ ์ž์—ฐ์–ด ๋ฌธ์žฅ๊ณผ ๊ฐ™์ด ๋‹จ์–ด์˜ ์ˆœ์„œ์— ๋”ฐ๋ผ ์˜๋ฏธ๊ฐ€ ๋‹ฌ๋ผ์ง€๋Š” ์ˆœ์ฐจ ๋ฐ์ดํ„ฐ(Sequential Data)๋ฅผ ๋‹ค๋ฃฐ ๋•Œ ์ฃผ๋กœ ์‚ฌ์šฉ๋˜๋Š” ์‹ ๊ฒฝ๋ง์ž…๋‹ˆ๋‹ค. ๋”ฐ๋ผ์„œ RNN์€ ๋‹จ์–ด์˜ ์–ด์ˆœ์— ๋”ฐ๋ผ ๋ฌธ์žฅ์˜ ์˜๋ฏธ๊ฐ€ ๋‹ฌ๋ผ์ง€๊ณ  ์•ž์— ์–ด๋–ค ๋‹จ์–ด๊ฐ€ ์“ฐ์˜€๋Š”์ง€ ๊ธฐ์–ตํ•ด์•ผ ๋’ค์— ์˜ค๋Š” ๋‹จ์–ด๋ฅผ ์˜ˆ์ธกํ•˜๋Š” ๋“ฑ์˜ ๋ฌธ์ œ๋ฅผ ํ’€ ๋•Œ ์ฃผ๋กœ ํ™œ์šฉ๋ฉ๋‹ˆ๋‹ค. RNN์€ Hidden Layer์˜ ๋…ธ๋“œ์—์„œ ํ™œ์„ฑํ™” ํ•จ์ˆ˜(Activation Function)๋ฅผ ๊ฑฐ์ณ ๋‚˜์˜จ ๊ฒฐ๊ด๊ฐ’์„ Output Layer๋กœ ๋ณด๋‚ด๋ฉด์„œ ๋‹ค์‹œ ๋‹ค์Œ Hidden Layer ๋…ธ๋“œ ๊ณ„์‚ฐ์˜ ์ž…๋ ฅ๊ฐ’์œผ๋กœ ๋ณด๋‚ด๋Š” ์‹ ๊ฒฝ๋ง์ž…๋‹ˆ๋‹ค. ์ด์ฒ˜๋Ÿผ, ๊ฒฐ๊ด๊ฐ’์ด ๋‹ค์Œ Hidden Layer ๋…ธ๋“œ์˜..