{"id":477893,"date":"2023-08-09T09:22:01","date_gmt":"2023-08-09T09:22:01","guid":{"rendered":""},"modified":"2023-09-05T11:15:37","modified_gmt":"2023-09-05T11:15:37","slug":"loss-functions","status":"publish","type":"wiki","link":"https:\/\/oneproxy.pro\/vn\/wiki\/loss-functions\/","title":{"rendered":"H\u00e0m m\u1ea5t m\u00e1t"},"content":{"rendered":"

Trong l\u0129nh v\u1ef1c h\u1ecdc m\u00e1y v\u00e0 tr\u00ed tu\u1ec7 nh\u00e2n t\u1ea1o, h\u00e0m m\u1ea5t m\u00e1t \u0111\u00f3ng vai tr\u00f2 c\u01a1 b\u1ea3n. C\u00e1c h\u00e0m to\u00e1n h\u1ecdc n\u00e0y \u0111\u00f3ng vai tr\u00f2 l\u00e0 th\u01b0\u1edbc \u0111o s\u1ef1 kh\u00e1c bi\u1ec7t gi\u1eefa k\u1ebft qu\u1ea3 \u0111\u1ea7u ra \u0111\u01b0\u1ee3c d\u1ef1 \u0111o\u00e1n v\u00e0 gi\u00e1 tr\u1ecb th\u1ef1c t\u1ebf c\u01a1 b\u1ea3n, cho ph\u00e9p c\u00e1c m\u00f4 h\u00ecnh h\u1ecdc m\u00e1y t\u1ed1i \u01b0u h\u00f3a c\u00e1c tham s\u1ed1 c\u1ee7a ch\u00fang v\u00e0 \u0111\u01b0a ra d\u1ef1 \u0111o\u00e1n ch\u00ednh x\u00e1c. H\u00e0m m\u1ea5t m\u00e1t l\u00e0 th\u00e0nh ph\u1ea7n thi\u1ebft y\u1ebfu c\u1ee7a nhi\u1ec1u nhi\u1ec7m v\u1ee5 kh\u00e1c nhau, bao g\u1ed3m h\u1ed3i quy, ph\u00e2n lo\u1ea1i v\u00e0 hu\u1ea5n luy\u1ec7n m\u1ea1ng l\u01b0\u1edbi th\u1ea7n kinh.<\/p>\n

L\u1ecbch s\u1eed v\u1ec1 ngu\u1ed3n g\u1ed1c c\u1ee7a h\u00e0m Loss v\u00e0 l\u1ea7n \u0111\u1ea7u ti\u00ean \u0111\u1ec1 c\u1eadp \u0111\u1ebfn n\u00f3.<\/h2>\n

Kh\u00e1i ni\u1ec7m v\u1ec1 h\u00e0m m\u1ea5t m\u00e1t c\u00f3 th\u1ec3 b\u1eaft ngu\u1ed3n t\u1eeb nh\u1eefng ng\u00e0y \u0111\u1ea7u c\u1ee7a l\u00fd thuy\u1ebft th\u1ed1ng k\u00ea v\u00e0 t\u1ed1i \u01b0u h\u00f3a. Ngu\u1ed3n g\u1ed1c c\u1ee7a h\u00e0m m\u1ea5t m\u00e1t n\u1eb1m trong c\u00e1c c\u00f4ng tr\u00ecnh c\u1ee7a Gauss v\u00e0 Laplace v\u00e0o th\u1ebf k\u1ef7 18 v\u00e0 19, n\u01a1i h\u1ecd \u0111\u01b0a ra ph\u01b0\u01a1ng ph\u00e1p b\u00ecnh ph\u01b0\u01a1ng t\u1ed1i thi\u1ec3u, nh\u1eb1m m\u1ee5c \u0111\u00edch gi\u1ea3m thi\u1ec3u t\u1ed5ng b\u00ecnh ph\u01b0\u01a1ng ch\u00eanh l\u1ec7ch gi\u1eefa c\u00e1c quan s\u00e1t v\u00e0 gi\u00e1 tr\u1ecb k\u1ef3 v\u1ecdng c\u1ee7a ch\u00fang.<\/p>\n

Trong b\u1ed1i c\u1ea3nh h\u1ecdc m\u00e1y, thu\u1eadt ng\u1eef \u201ch\u00e0m m\u1ea5t m\u00e1t\u201d \u0111\u00e3 tr\u1edf n\u00ean n\u1ed5i b\u1eadt trong qu\u00e1 tr\u00ecnh ph\u00e1t tri\u1ec3n c\u00e1c m\u00f4 h\u00ecnh h\u1ed3i quy tuy\u1ebfn t\u00ednh v\u00e0o gi\u1eefa th\u1ebf k\u1ef7 20. C\u00e1c c\u00f4ng tr\u00ecnh c\u1ee7a Abraham Wald v\u00e0 Ronald Fisher \u0111\u00e3 \u0111\u00f3ng g\u00f3p \u0111\u00e1ng k\u1ec3 v\u00e0o s\u1ef1 hi\u1ec3u bi\u1ebft v\u00e0 ch\u00ednh th\u1ee9c h\u00f3a c\u00e1c h\u00e0m t\u1ed5n th\u1ea5t trong \u01b0\u1edbc l\u01b0\u1ee3ng th\u1ed1ng k\u00ea v\u00e0 l\u00fd thuy\u1ebft quy\u1ebft \u0111\u1ecbnh.<\/p>\n

Th\u00f4ng tin chi ti\u1ebft v\u1ec1 c\u00e1c h\u00e0m Loss. M\u1edf r\u1ed9ng ch\u1ee7 \u0111\u1ec1 Ch\u1ee9c n\u0103ng m\u1ea5t m\u00e1t.<\/h2>\n

H\u00e0m m\u1ea5t m\u00e1t l\u00e0 x\u01b0\u01a1ng s\u1ed1ng c\u1ee7a c\u00e1c thu\u1eadt to\u00e1n h\u1ecdc c\u00f3 gi\u00e1m s\u00e1t. Ch\u00fang \u0111\u1ecbnh l\u01b0\u1ee3ng l\u1ed7i ho\u1eb7c s\u1ef1 kh\u00e1c bi\u1ec7t gi\u1eefa gi\u00e1 tr\u1ecb d\u1ef1 \u0111o\u00e1n v\u00e0 m\u1ee5c ti\u00eau th\u1ef1c t\u1ebf, cung c\u1ea5p ph\u1ea3n h\u1ed3i c\u1ea7n thi\u1ebft \u0111\u1ec3 c\u1eadp nh\u1eadt c\u00e1c tham s\u1ed1 m\u00f4 h\u00ecnh trong qu\u00e1 tr\u00ecnh \u0111\u00e0o t\u1ea1o. M\u1ee5c ti\u00eau c\u1ee7a vi\u1ec7c \u0111\u00e0o t\u1ea1o m\u00f4 h\u00ecnh h\u1ecdc m\u00e1y l\u00e0 gi\u1ea3m thi\u1ec3u h\u00e0m m\u1ea5t m\u00e1t \u0111\u1ec3 \u0111\u1ea1t \u0111\u01b0\u1ee3c nh\u1eefng d\u1ef1 \u0111o\u00e1n ch\u00ednh x\u00e1c v\u00e0 \u0111\u00e1ng tin c\u1eady v\u1ec1 d\u1eef li\u1ec7u ch\u01b0a nh\u00ecn th\u1ea5y.<\/p>\n

Trong b\u1ed1i c\u1ea3nh h\u1ecdc s\u00e2u v\u00e0 m\u1ea1ng l\u01b0\u1edbi th\u1ea7n kinh, c\u00e1c h\u00e0m m\u1ea5t m\u00e1t \u0111\u00f3ng m\u1ed9t vai tr\u00f2 quan tr\u1ecdng trong lan truy\u1ec1n ng\u01b0\u1ee3c, trong \u0111\u00f3 \u0111\u1ed9 d\u1ed1c \u0111\u01b0\u1ee3c t\u00ednh to\u00e1n v\u00e0 s\u1eed d\u1ee5ng \u0111\u1ec3 c\u1eadp nh\u1eadt tr\u1ecdng s\u1ed1 c\u1ee7a c\u00e1c l\u1edbp m\u1ea1ng l\u01b0\u1edbi th\u1ea7n kinh. Vi\u1ec7c l\u1ef1a ch\u1ecdn h\u00e0m m\u1ea5t th\u00edch h\u1ee3p ph\u1ee5 thu\u1ed9c v\u00e0o b\u1ea3n ch\u1ea5t c\u1ee7a nhi\u1ec7m v\u1ee5, ch\u1eb3ng h\u1ea1n nh\u01b0 h\u1ed3i quy ho\u1eb7c ph\u00e2n lo\u1ea1i v\u00e0 c\u00e1c \u0111\u1eb7c \u0111i\u1ec3m c\u1ee7a t\u1eadp d\u1eef li\u1ec7u.<\/p>\n

C\u1ea5u tr\u00fac b\u00ean trong c\u1ee7a h\u00e0m Loss. C\u00e1ch ho\u1ea1t \u0111\u1ed9ng c\u1ee7a c\u00e1c h\u00e0m M\u1ea5t.<\/h2>\n

H\u00e0m t\u1ed5n th\u1ea5t th\u01b0\u1eddng c\u00f3 d\u1ea1ng ph\u01b0\u01a1ng tr\u00ecnh to\u00e1n h\u1ecdc \u0111\u1ec3 \u0111o l\u01b0\u1eddng s\u1ef1 kh\u00e1c bi\u1ec7t gi\u1eefa k\u1ebft qu\u1ea3 \u0111\u1ea7u ra \u0111\u01b0\u1ee3c d\u1ef1 \u0111o\u00e1n v\u00e0 nh\u00e3n ch\u00e2n l\u00fd c\u01a1 b\u1ea3n. Cho m\u1ed9t t\u1eadp d\u1eef li\u1ec7u c\u00f3 \u0111\u1ea7u v\u00e0o (X) v\u00e0 m\u1ee5c ti\u00eau t\u01b0\u01a1ng \u1ee9ng (Y), h\u00e0m m\u1ea5t m\u00e1t (L) \u00e1nh x\u1ea1 c\u00e1c d\u1ef1 \u0111o\u00e1n c\u1ee7a m\u00f4 h\u00ecnh (\u0177) th\u00e0nh m\u1ed9t gi\u00e1 tr\u1ecb v\u00f4 h\u01b0\u1edbng duy nh\u1ea5t bi\u1ec3u th\u1ecb l\u1ed7i:<\/p>\n

L(\u0177, Y)<\/p>\n

Qu\u00e1 tr\u00ecnh hu\u1ea5n luy\u1ec7n bao g\u1ed3m vi\u1ec7c \u0111i\u1ec1u ch\u1ec9nh c\u00e1c tham s\u1ed1 c\u1ee7a m\u00f4 h\u00ecnh \u0111\u1ec3 gi\u1ea3m thi\u1ec3u l\u1ed7i n\u00e0y. C\u00e1c h\u00e0m m\u1ea5t m\u00e1t th\u01b0\u1eddng \u0111\u01b0\u1ee3c s\u1eed d\u1ee5ng bao g\u1ed3m L\u1ed7i b\u00ecnh ph\u01b0\u01a1ng trung b\u00ecnh (MSE) cho c\u00e1c t\u00e1c v\u1ee5 h\u1ed3i quy v\u00e0 M\u1ea5t Entropy ch\u00e9o cho c\u00e1c t\u00e1c v\u1ee5 ph\u00e2n lo\u1ea1i.<\/p>\n

Ph\u00e2n t\u00edch c\u00e1c t\u00ednh n\u0103ng ch\u00ednh c\u1ee7a h\u00e0m Loss.<\/h2>\n

H\u00e0m m\u1ea5t c\u00f3 m\u1ed9t s\u1ed1 t\u00ednh n\u0103ng ch\u00ednh \u1ea3nh h\u01b0\u1edfng \u0111\u1ebfn vi\u1ec7c s\u1eed d\u1ee5ng v\u00e0 hi\u1ec7u qu\u1ea3 c\u1ee7a ch\u00fang trong c\u00e1c t\u00ecnh hu\u1ed1ng kh\u00e1c nhau:<\/p>\n

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    Li\u00ean t\u1ee5c<\/strong>: C\u00e1c h\u00e0m m\u1ea5t m\u00e1t ph\u1ea3i li\u00ean t\u1ee5c \u0111\u1ec3 c\u00f3 th\u1ec3 t\u1ed1i \u01b0u h\u00f3a m\u01b0\u1ee3t m\u00e0 v\u00e0 tr\u00e1nh c\u00e1c v\u1ea5n \u0111\u1ec1 h\u1ed9i t\u1ee5 trong qu\u00e1 tr\u00ecnh hu\u1ea5n luy\u1ec7n.<\/p>\n<\/li>\n

  2. \n

    Kh\u1ea3 n\u0103ng kh\u00e1c bi\u1ec7t<\/strong>: Kh\u1ea3 n\u0103ng vi ph\u00e2n l\u00e0 r\u1ea5t quan tr\u1ecdng \u0111\u1ed1i v\u1edbi thu\u1eadt to\u00e1n lan truy\u1ec1n ng\u01b0\u1ee3c \u0111\u1ec3 t\u00ednh to\u00e1n \u0111\u1ed9 d\u1ed1c m\u1ed9t c\u00e1ch hi\u1ec7u qu\u1ea3.<\/p>\n<\/li>\n

  3. \n

    \u0111\u1ed9 l\u1ed3i<\/strong>: H\u00e0m m\u1ea5t l\u1ed3i c\u00f3 m\u1ee9c t\u1ed1i thi\u1ec3u to\u00e0n c\u1ee5c duy nh\u1ea5t, gi\u00fap vi\u1ec7c t\u1ed1i \u01b0u h\u00f3a tr\u1edf n\u00ean \u0111\u01a1n gi\u1ea3n h\u01a1n.<\/p>\n<\/li>\n

  4. \n

    Nh\u1ea1y c\u1ea3m v\u1edbi c\u00e1c ngo\u1ea1i l\u1ec7<\/strong>: M\u1ed9t s\u1ed1 h\u00e0m m\u1ea5t m\u00e1t nh\u1ea1y c\u1ea3m h\u01a1n v\u1edbi c\u00e1c gi\u00e1 tr\u1ecb ngo\u1ea1i l\u1ec7, \u0111i\u1ec1u n\u00e0y c\u00f3 th\u1ec3 \u1ea3nh h\u01b0\u1edfng \u0111\u1ebfn hi\u1ec7u su\u1ea5t c\u1ee7a m\u00f4 h\u00ecnh khi c\u00f3 d\u1eef li\u1ec7u nhi\u1ec5u.<\/p>\n<\/li>\n

  5. \n

    Kh\u1ea3 n\u0103ng gi\u1ea3i th\u00edch<\/strong>: Trong m\u1ed9t s\u1ed1 \u1ee9ng d\u1ee5ng nh\u1ea5t \u0111\u1ecbnh, c\u00e1c h\u00e0m m\u1ea5t m\u00e1t c\u00f3 th\u1ec3 di\u1ec5n gi\u1ea3i c\u00f3 th\u1ec3 \u0111\u01b0\u1ee3c \u01b0u ti\u00ean \u0111\u1ec3 hi\u1ec3u r\u00f5 h\u01a1n v\u1ec1 h\u00e0nh vi c\u1ee7a m\u00f4 h\u00ecnh.<\/p>\n<\/li>\n<\/ol>\n

    C\u00e1c lo\u1ea1i h\u00e0m m\u1ea5t m\u00e1t<\/h2>\n

    H\u00e0m m\u1ea5t c\u00f3 nhi\u1ec1u lo\u1ea1i, m\u1ed7i lo\u1ea1i ph\u00f9 h\u1ee3p v\u1edbi c\u00e1c nhi\u1ec7m v\u1ee5 h\u1ecdc m\u00e1y c\u1ee5 th\u1ec3. D\u01b0\u1edbi \u0111\u00e2y l\u00e0 m\u1ed9t s\u1ed1 lo\u1ea1i h\u00e0m m\u1ea5t m\u00e1t ph\u1ed5 bi\u1ebfn:<\/p>\n\n\n\n\n\n\n\n\n\n
    M\u1ea5t ch\u1ee9c n\u0103ng<\/th>\nLo\u1ea1i nhi\u1ec7m v\u1ee5<\/th>\nC\u00f4ng th\u1ee9c<\/th>\n<\/tr>\n<\/thead>\n
    C\u00f3 ngh\u0129a l\u00e0 l\u1ed7i b\u00ecnh ph\u01b0\u01a1ng<\/td>\nh\u1ed3i quy<\/td>\nMSE(\u0177, Y) = (1\/n) \u03a3(\u0177 \u2013 Y)^2<\/td>\n<\/tr>\n
    M\u1ea5t Entropy ch\u00e9o<\/td>\nPh\u00e2n lo\u1ea1i<\/td>\nCE(\u0177, Y) = -\u03a3(Y * log(\u0177) + (1 \u2013 Y) * log(1 \u2013 \u0177))<\/td>\n<\/tr>\n
    M\u1ea5t b\u1ea3n l\u1ec1<\/td>\nM\u00e1y Vector h\u1ed7 tr\u1ee3<\/td>\nHL(\u0177, Y) = max(0, 1 \u2013 \u0177 * Y)<\/td>\n<\/tr>\n
    M\u1ea5t Huber<\/td>\nH\u1ed3i quy m\u1ea1nh m\u1ebd<\/td>\nHL(\u0177, Y) = { 0,5 * (\u0177 \u2013 Y)^2 cho<\/td>\n<\/tr>\n
    M\u1ea5t x\u00fac x\u1eafc<\/td>\nPh\u00e2n \u0111o\u1ea1n h\u00ecnh \u1ea3nh<\/td>\nDL(\u0177, Y) = 1 \u2013 (2 * \u03a3(\u0177 * Y) + \u025b) \/ (\u03a3\u0177 + \u03a3Y + \u025b)<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

    C\u00e1ch s\u1eed d\u1ee5ng H\u00e0m m\u1ea5t m\u00e1t, v\u1ea5n \u0111\u1ec1 v\u00e0 gi\u1ea3i ph\u00e1p li\u00ean quan \u0111\u1ebfn vi\u1ec7c s\u1eed d\u1ee5ng.<\/h2>\n

    Vi\u1ec7c l\u1ef1a ch\u1ecdn h\u00e0m m\u1ea5t th\u00edch h\u1ee3p l\u00e0 r\u1ea5t quan tr\u1ecdng cho s\u1ef1 th\u00e0nh c\u00f4ng c\u1ee7a m\u00f4 h\u00ecnh h\u1ecdc m\u00e1y. Tuy nhi\u00ean, vi\u1ec7c ch\u1ecdn h\u00e0m m\u1ea5t m\u00e1t ph\u00f9 h\u1ee3p c\u00f3 th\u1ec3 l\u00e0 m\u1ed9t th\u00e1ch th\u1ee9c v\u00e0 ph\u1ee5 thu\u1ed9c v\u00e0o c\u00e1c y\u1ebfu t\u1ed1 nh\u01b0 b\u1ea3n ch\u1ea5t c\u1ee7a d\u1eef li\u1ec7u, ki\u1ebfn tr\u00fac m\u00f4 h\u00ecnh v\u00e0 \u0111\u1ea7u ra mong mu\u1ed1n.<\/p>\n

    Nh\u1eefng th\u00e1ch th\u1ee9c:<\/strong><\/p>\n

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      M\u1ea5t c\u00e2n b\u1eb1ng l\u1edbp<\/strong>: Trong c\u00e1c nhi\u1ec7m v\u1ee5 ph\u00e2n lo\u1ea1i, vi\u1ec7c ph\u00e2n b\u1ed5 l\u1edbp kh\u00f4ng c\u00e2n b\u1eb1ng c\u00f3 th\u1ec3 d\u1eabn \u0111\u1ebfn c\u00e1c m\u00f4 h\u00ecnh sai l\u1ec7ch. Gi\u1ea3i quy\u1ebft v\u1ea5n \u0111\u1ec1 n\u00e0y b\u1eb1ng c\u00e1ch s\u1eed d\u1ee5ng c\u00e1c h\u00e0m ho\u1eb7c k\u1ef9 thu\u1eadt gi\u1ea3m tr\u1ecdng s\u1ed1 nh\u01b0 l\u1ea5y m\u1eabu qu\u00e1 m\u1ee9c v\u00e0 l\u1ea5y m\u1eabu d\u01b0\u1edbi m\u1ee9c.<\/p>\n<\/li>\n

    2. \n

      Trang b\u1ecb qu\u00e1 m\u1ee9c<\/strong>: M\u1ed9t s\u1ed1 h\u00e0m m\u1ea5t m\u00e1t c\u00f3 th\u1ec3 l\u00e0m tr\u1ea7m tr\u1ecdng th\u00eam t\u00ecnh tr\u1ea1ng qu\u00e1 kh\u1edbp, d\u1eabn \u0111\u1ebfn t\u00ednh kh\u00e1i qu\u00e1t h\u00f3a k\u00e9m. C\u00e1c k\u1ef9 thu\u1eadt ch\u00ednh quy h\u00f3a nh\u01b0 ch\u00ednh quy h\u00f3a L1 v\u00e0 L2 c\u00f3 th\u1ec3 gi\u00fap gi\u1ea3m b\u1edbt vi\u1ec7c trang b\u1ecb qu\u00e1 m\u1ee9c.<\/p>\n<\/li>\n

    3. \n

      D\u1eef li\u1ec7u \u0111a ph\u01b0\u01a1ng th\u1ee9c<\/strong>: Khi x\u1eed l\u00fd d\u1eef li\u1ec7u \u0111a ph\u01b0\u01a1ng th\u1ee9c, c\u00e1c m\u00f4 h\u00ecnh c\u00f3 th\u1ec3 g\u1eb7p kh\u00f3 kh\u0103n trong vi\u1ec7c h\u1ed9i t\u1ee5 do c\u00f3 nhi\u1ec1u gi\u1ea3i ph\u00e1p t\u1ed1i \u01b0u. Kh\u00e1m ph\u00e1 c\u00e1c h\u00e0m m\u1ea5t t\u00f9y ch\u1ec9nh ho\u1eb7c c\u00e1c m\u00f4 h\u00ecnh t\u1ed5ng qu\u00e1t c\u00f3 th\u1ec3 c\u00f3 \u00edch.<\/p>\n<\/li>\n<\/ol>\n

      C\u00e1c gi\u1ea3i ph\u00e1p:<\/strong><\/p>\n

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        Ch\u1ee9c n\u0103ng m\u1ea5t t\u00f9y ch\u1ec9nh<\/strong>: Vi\u1ec7c thi\u1ebft k\u1ebf c\u00e1c h\u00e0m m\u1ea5t m\u00e1t d\u00e0nh ri\u00eang cho nhi\u1ec7m v\u1ee5 c\u00f3 th\u1ec3 \u0111i\u1ec1u ch\u1ec9nh h\u00e0nh vi c\u1ee7a m\u00f4 h\u00ecnh \u0111\u1ec3 \u0111\u00e1p \u1ee9ng c\u00e1c y\u00eau c\u1ea7u c\u1ee5 th\u1ec3.<\/p>\n<\/li>\n

      2. \n

        H\u1ecdc s\u1ed1 li\u1ec7u<\/strong>: Trong c\u00e1c t\u00ecnh hu\u1ed1ng m\u00e0 vi\u1ec7c gi\u00e1m s\u00e1t tr\u1ef1c ti\u1ebfp b\u1ecb h\u1ea1n ch\u1ebf, c\u00e1c h\u00e0m m\u1ea5t m\u00e1t h\u1ecdc theo h\u1ec7 m\u00e9t c\u00f3 th\u1ec3 \u0111\u01b0\u1ee3c s\u1eed d\u1ee5ng \u0111\u1ec3 t\u00ecm hi\u1ec3u s\u1ef1 t\u01b0\u01a1ng \u0111\u1ed3ng ho\u1eb7c kho\u1ea3ng c\u00e1ch gi\u1eefa c\u00e1c m\u1eabu.<\/p>\n<\/li>\n

      3. \n

        Ch\u1ee9c n\u0103ng m\u1ea5t th\u00edch \u1ee9ng<\/strong>: C\u00e1c k\u1ef9 thu\u1eadt nh\u01b0 m\u1ea5t ti\u00eau \u0111i\u1ec3m \u0111i\u1ec1u ch\u1ec9nh tr\u1ecdng l\u01b0\u1ee3ng b\u1ecb m\u1ea5t d\u1ef1a tr\u00ean \u0111\u1ed9 kh\u00f3 c\u1ee7a t\u1eebng m\u1eabu, \u01b0u ti\u00ean c\u00e1c m\u1eabu kh\u00f3 trong qu\u00e1 tr\u00ecnh \u0111\u00e0o t\u1ea1o.<\/p>\n<\/li>\n<\/ol>\n

        C\u00e1c \u0111\u1eb7c \u0111i\u1ec3m ch\u00ednh v\u00e0 c\u00e1c so s\u00e1nh kh\u00e1c v\u1edbi c\u00e1c thu\u1eadt ng\u1eef t\u01b0\u01a1ng t\u1ef1 d\u01b0\u1edbi d\u1ea1ng b\u1ea3ng v\u00e0 danh s\u00e1ch.<\/h2>\n\n\n\n\n\n\n\n\n\n\n
        Thu\u1eadt ng\u1eef<\/th>\nS\u1ef1 mi\u00eau t\u1ea3<\/th>\n<\/tr>\n<\/thead>\n
        M\u1ea5t ch\u1ee9c n\u0103ng<\/td>\n\u0110o l\u01b0\u1eddng s\u1ef1 kh\u00e1c bi\u1ec7t gi\u1eefa gi\u00e1 tr\u1ecb d\u1ef1 \u0111o\u00e1n v\u00e0 gi\u00e1 tr\u1ecb th\u1ef1c t\u1ebf trong \u0111\u00e0o t\u1ea1o m\u00e1y h\u1ecdc.<\/td>\n<\/tr>\n
        Ch\u1ee9c n\u0103ng \u01b0\u1edbc l\u01b0\u1ee3ng<\/td>\n\u0110\u01b0\u1ee3c s\u1eed d\u1ee5ng trong c\u00e1c thu\u1eadt to\u00e1n t\u1ed1i \u01b0u h\u00f3a \u0111\u1ec3 t\u00ecm c\u00e1c tham s\u1ed1 m\u00f4 h\u00ecnh t\u1ed1i \u01b0u.<\/td>\n<\/tr>\n
        H\u00e0m m\u1ee5c ti\u00eau<\/td>\nTh\u1ec3 hi\u1ec7n m\u1ee5c ti\u00eau \u0111\u01b0\u1ee3c t\u1ed1i \u01b0u h\u00f3a trong c\u00e1c t\u00e1c v\u1ee5 h\u1ecdc m\u00e1y.<\/td>\n<\/tr>\n
        M\u1ea5t ch\u00ednh quy<\/td>\nTh\u1eddi h\u1ea1n ph\u1ea1t b\u1ed5 sung \u0111\u1ec3 ng\u0103n ch\u1eb7n vi\u1ec7c trang b\u1ecb qu\u00e1 m\u1ee9c b\u1eb1ng c\u00e1ch kh\u00f4ng khuy\u1ebfn kh\u00edch c\u00e1c gi\u00e1 tr\u1ecb tham s\u1ed1 l\u1edbn.<\/td>\n<\/tr>\n
        R\u1ee7i ro th\u1ef1c nghi\u1ec7m<\/td>\nGi\u00e1 tr\u1ecb h\u00e0m m\u1ea5t m\u00e1t trung b\u00ecnh \u0111\u01b0\u1ee3c t\u00ednh to\u00e1n tr\u00ean t\u1eadp d\u1eef li\u1ec7u hu\u1ea5n luy\u1ec7n.<\/td>\n<\/tr>\n
        Thu th\u1eadp th\u00f4ng tin<\/td>\nTrong c\u00e2y quy\u1ebft \u0111\u1ecbnh, \u0111o l\u01b0\u1eddng m\u1ee9c \u0111\u1ed9 gi\u1ea3m entropy do m\u1ed9t thu\u1ed9c t\u00ednh c\u1ee5 th\u1ec3.<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

        C\u00e1c quan \u0111i\u1ec3m v\u00e0 c\u00f4ng ngh\u1ec7 c\u1ee7a t\u01b0\u01a1ng lai li\u00ean quan \u0111\u1ebfn H\u00e0m m\u1ea5t m\u00e1t.<\/h2>\n

        Khi h\u1ecdc m\u00e1y v\u00e0 tr\u00ed tu\u1ec7 nh\u00e2n t\u1ea1o ti\u1ebfp t\u1ee5c ph\u00e1t tri\u1ec3n, s\u1ef1 ph\u00e1t tri\u1ec3n v\u00e0 c\u1ea3i ti\u1ebfn c\u00e1c ch\u1ee9c n\u0103ng m\u1ea5t m\u00e1t c\u0169ng s\u1ebd ph\u00e1t tri\u1ec3n. Tri\u1ec3n v\u1ecdng trong t\u01b0\u01a1ng lai c\u00f3 th\u1ec3 bao g\u1ed3m:<\/p>\n

          \n
        1. \n

          Ch\u1ee9c n\u0103ng m\u1ea5t th\u00edch \u1ee9ng<\/strong>: T\u1ef1 \u0111\u1ed9ng \u0111i\u1ec1u ch\u1ec9nh c\u00e1c h\u00e0m m\u1ea5t trong qu\u00e1 tr\u00ecnh \u0111\u00e0o t\u1ea1o \u0111\u1ec3 n\u00e2ng cao hi\u1ec7u su\u1ea5t m\u00f4 h\u00ecnh tr\u00ean c\u00e1c ph\u00e2n ph\u1ed1i d\u1eef li\u1ec7u c\u1ee5 th\u1ec3.<\/p>\n<\/li>\n

        2. \n

          H\u00e0m m\u1ea5t m\u00e1t nh\u1eadn bi\u1ebft s\u1ef1 kh\u00f4ng ch\u1eafc ch\u1eafn<\/strong>: Gi\u1edbi thi\u1ec7u \u01b0\u1edbc t\u00ednh \u0111\u1ed9 kh\u00f4ng \u0111\u1ea3m b\u1ea3o trong c\u00e1c h\u00e0m m\u1ea5t m\u00e1t \u0111\u1ec3 x\u1eed l\u00fd c\u00e1c \u0111i\u1ec3m d\u1eef li\u1ec7u m\u01a1 h\u1ed3 m\u1ed9t c\u00e1ch hi\u1ec7u qu\u1ea3.<\/p>\n<\/li>\n

        3. \n

          M\u1ea5t h\u1ecdc t\u1eadp c\u1ee7ng c\u1ed1<\/strong>: K\u1ebft h\u1ee3p c\u00e1c k\u1ef9 thu\u1eadt h\u1ecdc t\u0103ng c\u01b0\u1eddng \u0111\u1ec3 t\u1ed1i \u01b0u h\u00f3a c\u00e1c m\u00f4 h\u00ecnh cho c\u00e1c nhi\u1ec7m v\u1ee5 ra quy\u1ebft \u0111\u1ecbnh tu\u1ea7n t\u1ef1.<\/p>\n<\/li>\n

        4. \n

          H\u00e0m m\u1ea5t theo t\u00ean mi\u1ec1n c\u1ee5 th\u1ec3<\/strong>: \u0110i\u1ec1u ch\u1ec9nh c\u00e1c h\u00e0m m\u1ea5t m\u00e1t cho c\u00e1c mi\u1ec1n c\u1ee5 th\u1ec3, cho ph\u00e9p \u0111\u00e0o t\u1ea1o m\u00f4 h\u00ecnh hi\u1ec7u qu\u1ea3 v\u00e0 ch\u00ednh x\u00e1c h\u01a1n.<\/p>\n<\/li>\n<\/ol>\n

          C\u00e1ch s\u1eed d\u1ee5ng ho\u1eb7c li\u00ean k\u1ebft m\u00e1y ch\u1ee7 proxy v\u1edbi ch\u1ee9c n\u0103ng M\u1ea5t.<\/h2>\n

          M\u00e1y ch\u1ee7 proxy \u0111\u00f3ng m\u1ed9t vai tr\u00f2 quan tr\u1ecdng trong c\u00e1c kh\u00eda c\u1ea1nh kh\u00e1c nhau c\u1ee7a h\u1ecdc m\u00e1y v\u00e0 m\u1ed1i li\u00ean h\u1ec7 c\u1ee7a ch\u00fang v\u1edbi c\u00e1c h\u00e0m m\u1ea5t m\u00e1t c\u00f3 th\u1ec3 \u0111\u01b0\u1ee3c th\u1ea5y trong m\u1ed9t s\u1ed1 tr\u01b0\u1eddng h\u1ee3p:<\/p>\n

            \n
          1. \n

            Thu th\u1eadp d\u1eef li\u1ec7u<\/strong>: M\u00e1y ch\u1ee7 proxy c\u00f3 th\u1ec3 \u0111\u01b0\u1ee3c s\u1eed d\u1ee5ng \u0111\u1ec3 \u1ea9n danh v\u00e0 ph\u00e2n ph\u1ed1i c\u00e1c y\u00eau c\u1ea7u thu th\u1eadp d\u1eef li\u1ec7u, gi\u00fap x\u00e2y d\u1ef1ng c\u00e1c b\u1ed9 d\u1eef li\u1ec7u \u0111a d\u1ea1ng v\u00e0 kh\u00f4ng thi\u00ean v\u1ecb \u0111\u1ec3 \u0111\u00e0o t\u1ea1o c\u00e1c m\u00f4 h\u00ecnh h\u1ecdc m\u00e1y.<\/p>\n<\/li>\n

          2. \n

            T\u0103ng c\u01b0\u1eddng d\u1eef li\u1ec7u<\/strong>: Proxy c\u00f3 th\u1ec3 h\u1ed7 tr\u1ee3 t\u0103ng c\u01b0\u1eddng d\u1eef li\u1ec7u b\u1eb1ng c\u00e1ch thu th\u1eadp d\u1eef li\u1ec7u t\u1eeb nhi\u1ec1u v\u1ecb tr\u00ed \u0111\u1ecba l\u00fd kh\u00e1c nhau, l\u00e0m phong ph\u00fa t\u1eadp d\u1eef li\u1ec7u v\u00e0 gi\u1ea3m t\u00ecnh tr\u1ea1ng qu\u00e1 kh\u1edbp.<\/p>\n<\/li>\n

          3. \n

            Quy\u1ec1n ri\u00eang t\u01b0 v\u00e0 b\u1ea3o m\u1eadt<\/strong>: Proxy gi\u00fap b\u1ea3o v\u1ec7 th\u00f4ng tin nh\u1ea1y c\u1ea3m trong qu\u00e1 tr\u00ecnh \u0111\u00e0o t\u1ea1o m\u00f4 h\u00ecnh, \u0111\u1ea3m b\u1ea3o tu\u00e2n th\u1ee7 c\u00e1c quy \u0111\u1ecbnh b\u1ea3o v\u1ec7 d\u1eef li\u1ec7u.<\/p>\n<\/li>\n

          4. \n

            Tri\u1ec3n khai m\u00f4 h\u00ecnh<\/strong>: M\u00e1y ch\u1ee7 proxy c\u00f3 th\u1ec3 h\u1ed7 tr\u1ee3 c\u00e2n b\u1eb1ng t\u1ea3i v\u00e0 ph\u00e2n ph\u1ed1i d\u1ef1 \u0111o\u00e1n m\u00f4 h\u00ecnh, \u0111\u1ea3m b\u1ea3o tri\u1ec3n khai hi\u1ec7u qu\u1ea3 v\u00e0 c\u00f3 th\u1ec3 m\u1edf r\u1ed9ng.<\/p>\n<\/li>\n<\/ol>\n

            Li\u00ean k\u1ebft li\u00ean quan<\/h2>\n

            \u0110\u1ec3 bi\u1ebft th\u00eam th\u00f4ng tin v\u1ec1 c\u00e1c h\u00e0m M\u1ea5t m\u00e1t v\u00e0 \u1ee9ng d\u1ee5ng c\u1ee7a ch\u00fang, b\u1ea1n c\u00f3 th\u1ec3 th\u1ea5y c\u00e1c t\u00e0i nguy\u00ean sau h\u1eefu \u00edch:<\/p>\n

              \n
            1. Stanford CS231n: M\u1ea1ng th\u1ea7n kinh t\u00edch ch\u1eadp \u0111\u1ec3 nh\u1eadn d\u1ea1ng h\u00ecnh \u1ea3nh<\/a><\/li>\n
            2. S\u00e1ch Deep Learning: Ch\u01b0\u01a1ng 5, M\u1ea1ng l\u01b0\u1edbi th\u1ea7n kinh v\u00e0 H\u1ecdc s\u00e2u<\/a><\/li>\n
            3. T\u00e0i li\u1ec7u Scikit-learn: H\u00e0m m\u1ea5t<\/a><\/li>\n
            4. H\u01b0\u1edbng t\u1edbi khoa h\u1ecdc d\u1eef li\u1ec7u: T\u00ecm hi\u1ec3u v\u1ec1 h\u00e0m m\u1ea5t m\u00e1t<\/a><\/li>\n<\/ol>\n

              Khi h\u1ecdc m\u00e1y v\u00e0 AI ti\u1ebfp t\u1ee5c ph\u00e1t tri\u1ec3n, c\u00e1c h\u00e0m m\u1ea5t s\u1ebd v\u1eabn l\u00e0 m\u1ed9t y\u1ebfu t\u1ed1 quan tr\u1ecdng trong vi\u1ec7c \u0111\u00e0o t\u1ea1o v\u00e0 t\u1ed1i \u01b0u h\u00f3a m\u00f4 h\u00ecnh. Vi\u1ec7c hi\u1ec3u c\u00e1c lo\u1ea1i h\u00e0m m\u1ea5t kh\u00e1c nhau v\u00e0 \u1ee9ng d\u1ee5ng c\u1ee7a ch\u00fang s\u1ebd gi\u00fap c\u00e1c nh\u00e0 khoa h\u1ecdc v\u00e0 nh\u00e0 nghi\u00ean c\u1ee9u d\u1eef li\u1ec7u x\u00e2y d\u1ef1ng c\u00e1c m\u00f4 h\u00ecnh h\u1ecdc m\u00e1y m\u1ea1nh m\u1ebd v\u00e0 ch\u00ednh x\u00e1c h\u01a1n \u0111\u1ec3 gi\u1ea3i quy\u1ebft c\u00e1c th\u00e1ch th\u1ee9c trong th\u1ebf gi\u1edbi th\u1ef1c.<\/p>","protected":false},"featured_media":468810,"menu_order":0,"template":"","meta":{"_acf_changed":false,"content-type":"","inline_featured_image":false,"footnotes":""},"class_list":["post-477893","wiki","type-wiki","status-publish","has-post-thumbnail","hentry"],"acf":{"faq_title":"Frequently Asked Questions about Loss functions: Understanding the Crucial Element in Machine Learning<\/mark>","faq_items":[{"question":"What are Loss functions, and why are they important in machine learning?","answer":"

              Loss functions are mathematical tools that measure the difference between predicted outputs and actual ground truth values in machine learning models. They play a crucial role in training algorithms, enabling models to optimize their parameters and make accurate predictions. By minimizing the loss function, models can achieve better performance on unseen data and solve various tasks, including regression and classification.<\/p>"},{"question":"How did Loss functions originate, and who first mentioned them?","answer":"

              The concept of loss functions can be traced back to the works of Gauss and Laplace in the 18th and 19th centuries, where they introduced the method of least squares to minimize the squared differences between observations and their expected values. In the context of machine learning, the term \"loss function\" gained prominence during the development of linear regression models in the mid-20th century. Abraham Wald and Ronald Fisher significantly contributed to the formalization of loss functions in statistical estimation and decision theory.<\/p>"},{"question":"What is the internal structure of Loss functions, and how do they work?","answer":"

              Loss functions are mathematical equations that measure the dissimilarity between predicted outputs and ground truth labels. Given a dataset with inputs and corresponding targets, a loss function maps the predictions of a model to a single scalar value representing the error. During training, the model adjusts its parameters to minimize this error, which is critical in backpropagation for neural network training.<\/p>"},{"question":"What are the main types of Loss functions, and when are they used?","answer":"

              There are various types of loss functions, each suited for specific machine learning tasks. Common ones include Mean Squared Error (MSE) for regression, Cross-Entropy Loss for classification, Hinge Loss for support vector machines, Huber Loss for robust regression, and Dice Loss for image segmentation.<\/p>"},{"question":"What are the key features of Loss functions, and how do they impact model training?","answer":"

              Loss functions possess essential characteristics, including continuity, differentiability, convexity, sensitivity to outliers, and interpretability. These features influence the model's optimization process, convergence, and generalization performance.<\/p>"},{"question":"What are the challenges related to using Loss functions, and how can they be addressed?","answer":"

              Challenges in using loss functions include dealing with class imbalance, overfitting, and multimodal data. Addressing these challenges may involve techniques such as weighted loss functions, regularization, custom loss designs, and metric learning.<\/p>"},{"question":"How can Loss functions evolve in the future of machine learning?","answer":"

              Future perspectives for Loss functions include adaptive loss functions that adjust during training, uncertainty-aware loss functions, reinforcement learning losses for sequential decision-making, and domain-specific loss functions tailored to specific applications.<\/p>"},{"question":"How are proxy servers associated with Loss functions and machine learning?","answer":"

              Proxy servers play a significant role in machine learning by aiding in data collection, data augmentation, privacy, security, and model deployment. They enable researchers and data scientists to build more diverse and robust machine learning models.<\/p>"},{"question":"Where can I find more information about Loss functions?","answer":"

              For more in-depth information about Loss functions and their applications, you can explore resources such as Stanford CS231n, Deep Learning Book's Chapter 5, Scikit-learn Documentation, and Towards Data Science articles on understanding loss functions. Additionally, OneProxy, the leading proxy server provider, offers valuable insights into the connection between Loss functions and their cutting-edge technologies.<\/p>"}]},"_links":{"self":[{"href":"https:\/\/oneproxy.pro\/vn\/wp-json\/wp\/v2\/wiki\/477893","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/oneproxy.pro\/vn\/wp-json\/wp\/v2\/wiki"}],"about":[{"href":"https:\/\/oneproxy.pro\/vn\/wp-json\/wp\/v2\/types\/wiki"}],"version-history":[{"count":0,"href":"https:\/\/oneproxy.pro\/vn\/wp-json\/wp\/v2\/wiki\/477893\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/oneproxy.pro\/vn\/wp-json\/wp\/v2\/media\/468810"}],"wp:attachment":[{"href":"https:\/\/oneproxy.pro\/vn\/wp-json\/wp\/v2\/media?parent=477893"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}