{"id":477239,"date":"2023-08-09T09:09:43","date_gmt":"2023-08-09T09:09:43","guid":{"rendered":""},"modified":"2023-10-30T17:12:07","modified_gmt":"2023-10-30T17:12:07","slug":"fine-tuning","status":"publish","type":"wiki","link":"https:\/\/oneproxy.pro\/vn\/wiki\/fine-tuning\/","title":{"rendered":"Tinh ch\u1ec9nh"},"content":{"rendered":"<p>Trong th\u1ebf gi\u1edbi h\u1ecdc m\u00e1y v\u00e0 tr\u00ed tu\u1ec7 nh\u00e2n t\u1ea1o, vi\u1ec7c tinh ch\u1ec9nh l\u00e0 m\u1ed9t ph\u1ea7n kh\u00f4ng th\u1ec3 thi\u1ebfu trong qu\u00e1 tr\u00ecnh t\u1ed1i \u01b0u h\u00f3a m\u00f4 h\u00ecnh. V\u1ec1 c\u01a1 b\u1ea3n, n\u00f3 li\u00ean quan \u0111\u1ebfn m\u1ed9t k\u1ef9 thu\u1eadt h\u1ecdc chuy\u1ec3n giao trong \u0111\u00f3 m\u1ed9t m\u00f4 h\u00ecnh \u0111\u01b0\u1ee3c \u0111\u00e0o t\u1ea1o tr\u01b0\u1edbc \u0111\u01b0\u1ee3c \u0111i\u1ec1u ch\u1ec9nh \u0111\u1ec3 ph\u00f9 h\u1ee3p v\u1edbi m\u1ed9t nhi\u1ec7m v\u1ee5 kh\u00e1c nh\u01b0ng c\u00f3 li\u00ean quan.<\/p>\n<h2>Ngu\u1ed3n g\u1ed1c v\u00e0 s\u1ef1 ph\u00e1t tri\u1ec3n c\u1ee7a tinh ch\u1ec9nh<\/h2>\n<p>Tinh ch\u1ec9nh, trong b\u1ed1i c\u1ea3nh h\u1ecdc m\u00e1y v\u00e0 h\u1ecdc s\u00e2u, xu\u1ea5t hi\u1ec7n t\u1eeb kh\u00e1i ni\u1ec7m h\u1ecdc chuy\u1ec3n giao. \u00dd t\u01b0\u1edfng l\u00e0 khai th\u00e1c s\u1ee9c m\u1ea1nh c\u1ee7a m\u1ed9t m\u00f4 h\u00ecnh \u0111\u00e3 \u0111\u01b0\u1ee3c \u0111\u00e0o t\u1ea1o, \u0111\u01b0\u1ee3c g\u1ecdi l\u00e0 m\u00f4 h\u00ecnh c\u01a1 s\u1edf, \u0111\u1ec3 \u0111\u00e0o t\u1ea1o m\u1ed9t m\u00f4 h\u00ecnh m\u1edbi cho m\u1ed9t nhi\u1ec7m v\u1ee5 kh\u00e1c nh\u01b0ng c\u00f3 li\u00ean quan. H\u1ecdc chuy\u1ec3n giao \u0111\u01b0\u1ee3c nh\u1eafc \u0111\u1ebfn l\u1ea7n \u0111\u1ea7u ti\u00ean v\u00e0o cu\u1ed1i nh\u1eefng n\u0103m 1990, nh\u01b0ng n\u00f3 ng\u00e0y c\u00e0ng tr\u1edf n\u00ean ph\u1ed5 bi\u1ebfn v\u1edbi s\u1ef1 ra \u0111\u1eddi c\u1ee7a h\u1ecdc s\u00e2u v\u00e0 d\u1eef li\u1ec7u l\u1edbn v\u00e0o nh\u1eefng n\u0103m 2010.<\/p>\n<h2>\u0110i s\u00e2u h\u01a1n v\u00e0o tinh ch\u1ec9nh<\/h2>\n<p>Tinh ch\u1ec9nh l\u00e0 m\u1ed9t qu\u00e1 tr\u00ecnh t\u1eadn d\u1ee5ng m\u00f4 h\u00ecnh \u0111\u01b0\u1ee3c \u0111\u00e0o t\u1ea1o tr\u01b0\u1edbc cho m\u1ed9t nhi\u1ec7m v\u1ee5 m\u1edbi m\u00e0 kh\u00f4ng c\u1ea7n b\u1eaft \u0111\u1ea7u l\u1ea1i t\u1eeb \u0111\u1ea7u. \u00dd t\u01b0\u1edfng c\u01a1 b\u1ea3n l\u00e0 s\u1eed d\u1ee5ng l\u1ea1i &#039;c\u00e1c t\u00ednh n\u0103ng&#039; m\u00e0 m\u00f4 h\u00ecnh \u0111\u01b0\u1ee3c \u0111\u00e0o t\u1ea1o tr\u01b0\u1edbc \u0111\u00e3 h\u1ecdc \u0111\u01b0\u1ee3c trong nhi\u1ec7m v\u1ee5 ban \u0111\u1ea7u sang m\u1ed9t nhi\u1ec7m v\u1ee5 m\u1edbi, nhi\u1ec7m v\u1ee5 n\u00e0y c\u00f3 th\u1ec3 kh\u00f4ng c\u00f3 s\u1eb5n nhi\u1ec1u d\u1eef li\u1ec7u \u0111\u01b0\u1ee3c g\u1eafn nh\u00e3n.<\/p>\n<p>Qu\u00e1 tr\u00ecnh n\u00e0y cung c\u1ea5p m\u1ed9t s\u1ed1 l\u1ee3i th\u1ebf. Th\u1ee9 nh\u1ea5t, n\u00f3 ti\u1ebft ki\u1ec7m \u0111\u00e1ng k\u1ec3 th\u1eddi gian v\u00e0 t\u00e0i nguy\u00ean t\u00ednh to\u00e1n so v\u1edbi vi\u1ec7c \u0111\u00e0o t\u1ea1o m\u1ed9t m\u00f4 h\u00ecnh deep learning t\u1eeb \u0111\u1ea7u. Th\u1ee9 hai, n\u00f3 cho ph\u00e9p ch\u00fang ta gi\u1ea3i quy\u1ebft c\u00e1c nhi\u1ec7m v\u1ee5 v\u1edbi d\u1eef li\u1ec7u \u00edt \u0111\u01b0\u1ee3c g\u1eafn nh\u00e3n h\u01a1n b\u1eb1ng c\u00e1ch t\u1eadn d\u1ee5ng c\u00e1c m\u1eabu m\u00e0 m\u00f4 h\u00ecnh c\u01a1 s\u1edf \u0111\u00e3 h\u1ecdc \u0111\u01b0\u1ee3c t\u1eeb c\u00e1c nhi\u1ec7m v\u1ee5 quy m\u00f4 l\u1edbn.<\/p>\n<h2>Ho\u1ea1t \u0111\u1ed9ng b\u00ean trong c\u1ee7a tinh ch\u1ec9nh<\/h2>\n<p>Vi\u1ec7c tinh ch\u1ec9nh th\u01b0\u1eddng \u0111\u01b0\u1ee3c th\u1ef1c hi\u1ec7n theo hai giai \u0111o\u1ea1n.<\/p>\n<ol>\n<li>Tr\u00edch xu\u1ea5t t\u00ednh n\u0103ng: \u1ede \u0111\u00e2y, m\u00f4 h\u00ecnh \u0111\u01b0\u1ee3c \u0111\u00e0o t\u1ea1o tr\u01b0\u1edbc \u0111\u01b0\u1ee3c c\u1ed1 \u0111\u1ecbnh v\u00e0 s\u1eed d\u1ee5ng l\u00e0m c\u00f4ng c\u1ee5 tr\u00edch xu\u1ea5t t\u00ednh n\u0103ng c\u1ed1 \u0111\u1ecbnh. \u0110\u1ea7u ra t\u1eeb m\u00f4 h\u00ecnh n\u00e0y \u0111\u01b0\u1ee3c \u0111\u01b0a v\u00e0o m\u1ed9t m\u00f4 h\u00ecnh m\u1edbi, th\u01b0\u1eddng l\u00e0 m\u1ed9t b\u1ed9 ph\u00e2n lo\u1ea1i \u0111\u01a1n gi\u1ea3n, sau \u0111\u00f3 \u0111\u01b0\u1ee3c hu\u1ea5n luy\u1ec7n v\u1ec1 nhi\u1ec7m v\u1ee5 m\u1edbi.<\/li>\n<li>Tinh ch\u1ec9nh: Sau khi tr\u00edch xu\u1ea5t t\u00ednh n\u0103ng, c\u00e1c l\u1edbp c\u1ee5 th\u1ec3 c\u1ee7a m\u00f4 h\u00ecnh (\u0111\u00f4i khi l\u00e0 to\u00e0n b\u1ed9 m\u00f4 h\u00ecnh) \u0111\u01b0\u1ee3c \u201cm\u1edf \u0111\u00f3ng b\u0103ng\u201d v\u00e0 m\u00f4 h\u00ecnh \u0111\u01b0\u1ee3c \u0111\u00e0o t\u1ea1o l\u1ea1i cho nhi\u1ec7m v\u1ee5 m\u1edbi. Trong giai \u0111o\u1ea1n n\u00e0y, t\u1ed1c \u0111\u1ed9 h\u1ecdc \u0111\u01b0\u1ee3c \u0111\u1eb7t \u1edf m\u1ee9c r\u1ea5t th\u1ea5p \u0111\u1ec3 tr\u00e1nh \u201cqu\u00ean\u201d nh\u1eefng t\u00ednh n\u0103ng h\u1eefu \u00edch \u0111\u00e3 h\u1ecdc \u1edf giai \u0111o\u1ea1n ti\u1ec1n \u0111\u00e0o t\u1ea1o.<\/li>\n<\/ol>\n<h2>C\u00e1c t\u00ednh n\u0103ng ch\u00ednh c\u1ee7a Tinh ch\u1ec9nh<\/h2>\n<ul>\n<li><strong>Chuy\u1ec3n giao ki\u1ebfn th\u1ee9c<\/strong>: Tinh ch\u1ec9nh chuy\u1ec3n giao ki\u1ebfn th\u1ee9c t\u1eeb nhi\u1ec7m v\u1ee5 n\u00e0y sang nhi\u1ec7m v\u1ee5 kh\u00e1c m\u1ed9t c\u00e1ch hi\u1ec7u qu\u1ea3, gi\u1ea3m nhu c\u1ea7u v\u1ec1 kh\u1ed1i l\u01b0\u1ee3ng l\u1edbn d\u1eef li\u1ec7u \u0111\u01b0\u1ee3c g\u1eafn nh\u00e3n cho nhi\u1ec7m v\u1ee5 m\u1edbi.<\/li>\n<li><strong>Hi\u1ec7u qu\u1ea3 t\u00ednh to\u00e1n<\/strong>: N\u00f3 \u00edt t\u1ed1n nhi\u1ec1u c\u00f4ng s\u1ee9c t\u00ednh to\u00e1n h\u01a1n so v\u1edbi vi\u1ec7c \u0111\u00e0o t\u1ea1o m\u1ed9t m\u00f4 h\u00ecnh deep learning t\u1eeb \u0111\u1ea7u.<\/li>\n<li><strong>Uy\u1ec3n chuy\u1ec3n<\/strong>: K\u1ef9 thu\u1eadt n\u00e0y linh ho\u1ea1t v\u00ec n\u00f3 c\u00f3 th\u1ec3 \u0111\u01b0\u1ee3c \u00e1p d\u1ee5ng cho c\u00e1c l\u1edbp kh\u00e1c nhau c\u1ee7a m\u00f4 h\u00ecnh \u0111\u01b0\u1ee3c \u0111\u00e0o t\u1ea1o tr\u01b0\u1edbc d\u1ef1a tr\u00ean s\u1ef1 gi\u1ed1ng nhau gi\u1eefa nhi\u1ec7m v\u1ee5 c\u01a1 b\u1ea3n v\u00e0 nhi\u1ec7m v\u1ee5 m\u1edbi.<\/li>\n<li><strong>C\u1ea3i thi\u1ec7n hi\u1ec7u su\u1ea5t<\/strong>: N\u00f3 th\u01b0\u1eddng d\u1eabn \u0111\u1ebfn hi\u1ec7u su\u1ea5t m\u00f4 h\u00ecnh \u0111\u01b0\u1ee3c c\u1ea3i thi\u1ec7n, \u0111\u1eb7c bi\u1ec7t khi d\u1eef li\u1ec7u c\u1ee7a nhi\u1ec7m v\u1ee5 m\u1edbi khan hi\u1ebfm ho\u1eb7c kh\u00f4ng \u0111\u1ee7 \u0111a d\u1ea1ng.<\/li>\n<\/ul>\n<h2>C\u00e1c lo\u1ea1i tinh ch\u1ec9nh<\/h2>\n<p>Ch\u1ee7 y\u1ebfu c\u00f3 hai lo\u1ea1i tinh ch\u1ec9nh:<\/p>\n<ol>\n<li><strong>Tinh ch\u1ec9nh d\u1ef1a tr\u00ean t\u00ednh n\u0103ng<\/strong>: \u1ede \u0111\u00e2y, m\u00f4 h\u00ecnh \u0111\u01b0\u1ee3c \u0111\u00e0o t\u1ea1o tr\u01b0\u1edbc \u0111\u01b0\u1ee3c s\u1eed d\u1ee5ng l\u00e0m c\u00f4ng c\u1ee5 tr\u00edch xu\u1ea5t t\u00ednh n\u0103ng c\u1ed1 \u0111\u1ecbnh trong khi m\u00f4 h\u00ecnh m\u1edbi \u0111\u01b0\u1ee3c \u0111\u00e0o t\u1ea1o b\u1eb1ng c\u00e1ch s\u1eed d\u1ee5ng c\u00e1c t\u00ednh n\u0103ng \u0111\u01b0\u1ee3c tr\u00edch xu\u1ea5t n\u00e0y.<\/li>\n<li><strong>Tinh ch\u1ec9nh \u0111\u1ea7y \u0111\u1ee7<\/strong>: Trong c\u00e1ch ti\u1ebfp c\u1eadn n\u00e0y, t\u1ea5t c\u1ea3 ho\u1eb7c c\u00e1c l\u1edbp c\u1ee5 th\u1ec3 c\u1ee7a m\u00f4 h\u00ecnh \u0111\u01b0\u1ee3c \u0111\u00e0o t\u1ea1o tr\u01b0\u1edbc s\u1ebd kh\u00f4ng b\u1ecb \u0111\u00f3ng b\u0103ng v\u00e0 \u0111\u01b0\u1ee3c \u0111\u00e0o t\u1ea1o cho nhi\u1ec7m v\u1ee5 m\u1edbi, v\u1edbi t\u1ef7 l\u1ec7 h\u1ecdc t\u1eadp th\u1ea5p \u0111\u1ec3 duy tr\u00ec c\u00e1c t\u00ednh n\u0103ng \u0111\u00e3 h\u1ecdc tr\u01b0\u1edbc.<\/li>\n<\/ol>\n<table>\n<thead>\n<tr>\n<th>Lo\u1ea1i tinh ch\u1ec9nh<\/th>\n<th>S\u1ef1 mi\u00eau t\u1ea3<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td>D\u1ef1a tr\u00ean t\u00ednh n\u0103ng<\/td>\n<td>M\u00f4 h\u00ecnh \u0111\u01b0\u1ee3c \u0111\u00e0o t\u1ea1o tr\u01b0\u1edbc \u0111\u01b0\u1ee3c s\u1eed d\u1ee5ng l\u00e0m tr\u00ecnh tr\u00edch xu\u1ea5t t\u00ednh n\u0103ng c\u1ed1 \u0111\u1ecbnh<\/td>\n<\/tr>\n<tr>\n<td>\u0110\u1ea7y<\/td>\n<td>C\u00e1c l\u1edbp c\u1ee5 th\u1ec3 ho\u1eb7c to\u00e0n b\u1ed9 m\u00f4 h\u00ecnh \u0111\u01b0\u1ee3c \u0111\u00e0o t\u1ea1o tr\u01b0\u1edbc \u0111\u01b0\u1ee3c \u0111\u00e0o t\u1ea1o l\u1ea1i cho nhi\u1ec7m v\u1ee5 m\u1edbi<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<h2>Tinh ch\u1ec9nh: \u1ee8ng d\u1ee5ng, th\u00e1ch th\u1ee9c v\u00e0 gi\u1ea3i ph\u00e1p<\/h2>\n<p>Tinh ch\u1ec9nh t\u00ecm th\u1ea5y c\u00e1c \u1ee9ng d\u1ee5ng r\u1ed9ng r\u00e3i trong c\u00e1c l\u0129nh v\u1ef1c h\u1ecdc m\u00e1y kh\u00e1c nhau nh\u01b0 th\u1ecb gi\u00e1c m\u00e1y t\u00ednh (ph\u00e1t hi\u1ec7n \u0111\u1ed1i t\u01b0\u1ee3ng, ph\u00e2n lo\u1ea1i h\u00ecnh \u1ea3nh), x\u1eed l\u00fd ng\u00f4n ng\u1eef t\u1ef1 nhi\u00ean (ph\u00e2n t\u00edch t\u00ecnh c\u1ea3m, ph\u00e2n lo\u1ea1i v\u0103n b\u1ea3n) v\u00e0 x\u1eed l\u00fd \u00e2m thanh (nh\u1eadn d\u1ea1ng gi\u1ecdng n\u00f3i).<\/p>\n<p>Tuy nhi\u00ean, n\u00f3 \u0111\u1eb7t ra m\u1ed9t s\u1ed1 th\u00e1ch th\u1ee9c:<\/p>\n<ol>\n<li><strong>S\u1ef1 l\u00e3ng qu\u00ean th\u1ea3m kh\u1ed1c<\/strong>: \u0110i\u1ec1u n\u00e0y \u0111\u1ec1 c\u1eadp \u0111\u1ebfn vi\u1ec7c m\u00f4 h\u00ecnh qu\u00ean c\u00e1c t\u00ednh n\u0103ng \u0111\u00e3 h\u1ecdc t\u1eeb nhi\u1ec7m v\u1ee5 c\u01a1 b\u1ea3n trong khi tinh ch\u1ec9nh nhi\u1ec7m v\u1ee5 m\u1edbi. Gi\u1ea3i ph\u00e1p cho v\u1ea5n \u0111\u1ec1 n\u00e0y l\u00e0 s\u1eed d\u1ee5ng t\u1ed1c \u0111\u1ed9 h\u1ecdc th\u1ea5p h\u01a1n trong qu\u00e1 tr\u00ecnh tinh ch\u1ec9nh.<\/li>\n<li><strong>Chuy\u1ec3n giao ti\u00eau c\u1ef1c<\/strong>: \u0110\u00e2y l\u00e0 l\u00fac ki\u1ebfn th\u1ee9c c\u1ee7a m\u00f4 h\u00ecnh c\u01a1 s\u1edf t\u00e1c \u0111\u1ed9ng ti\u00eau c\u1ef1c \u0111\u1ebfn hi\u1ec7u su\u1ea5t c\u1ee7a nhi\u1ec7m v\u1ee5 m\u1edbi. Gi\u1ea3i ph\u00e1p n\u1eb1m \u1edf vi\u1ec7c l\u1ef1a ch\u1ecdn c\u1ea9n th\u1eadn l\u1edbp n\u00e0o \u0111\u1ec3 tinh ch\u1ec9nh v\u00e0 s\u1eed d\u1ee5ng c\u00e1c l\u1edbp d\u00e0nh ri\u00eang cho nhi\u1ec7m v\u1ee5 khi c\u1ea7n thi\u1ebft.<\/li>\n<\/ol>\n<h2>So s\u00e1nh Tinh ch\u1ec9nh v\u1edbi c\u00e1c kh\u00e1i ni\u1ec7m li\u00ean quan<\/h2>\n<p>Tinh ch\u1ec9nh th\u01b0\u1eddng \u0111\u01b0\u1ee3c so s\u00e1nh v\u1edbi c\u00e1c kh\u00e1i ni\u1ec7m li\u00ean quan nh\u01b0:<\/p>\n<ul>\n<li><strong>Khai th\u00e1c t\u00ednh n\u0103ng<\/strong>: \u1ede \u0111\u00e2y, m\u00f4 h\u00ecnh c\u01a1 s\u1edf \u0111\u01b0\u1ee3c s\u1eed d\u1ee5ng ho\u00e0n to\u00e0n nh\u01b0 m\u1ed9t c\u00f4ng c\u1ee5 tr\u00edch xu\u1ea5t t\u00ednh n\u0103ng m\u00e0 kh\u00f4ng c\u1ea7n \u0111\u00e0o t\u1ea1o th\u00eam. Ng\u01b0\u1ee3c l\u1ea1i, tinh ch\u1ec9nh ti\u1ebfp t\u1ee5c qu\u00e1 tr\u00ecnh \u0111\u00e0o t\u1ea1o v\u1ec1 nhi\u1ec7m v\u1ee5 m\u1edbi.<\/li>\n<li><strong>Chuy\u1ec3n ti\u1ebfp h\u1ecdc t\u1eadp<\/strong>: M\u1eb7c d\u00f9 tinh ch\u1ec9nh l\u00e0 m\u1ed9t h\u00ecnh th\u1ee9c h\u1ecdc chuy\u1ec3n giao, nh\u01b0ng kh\u00f4ng ph\u1ea3i t\u1ea5t c\u1ea3 h\u1ecdc chuy\u1ec3n giao \u0111\u1ec1u li\u00ean quan \u0111\u1ebfn tinh ch\u1ec9nh. Trong m\u1ed9t s\u1ed1 tr\u01b0\u1eddng h\u1ee3p, ch\u1ec9 s\u1eed d\u1ee5ng ki\u1ebfn tr\u00fac c\u1ee7a m\u00f4 h\u00ecnh \u0111\u01b0\u1ee3c \u0111\u00e0o t\u1ea1o tr\u01b0\u1edbc v\u00e0 m\u00f4 h\u00ecnh \u0111\u01b0\u1ee3c \u0111\u00e0o t\u1ea1o l\u1ea1i t\u1eeb \u0111\u1ea7u cho nhi\u1ec7m v\u1ee5 m\u1edbi.<\/li>\n<\/ul>\n<table>\n<thead>\n<tr>\n<th>\u00dd t\u01b0\u1edfng<\/th>\n<th>S\u1ef1 mi\u00eau t\u1ea3<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td>Khai th\u00e1c t\u00ednh n\u0103ng<\/td>\n<td>S\u1eed d\u1ee5ng m\u00f4 h\u00ecnh c\u01a1 s\u1edf ho\u00e0n to\u00e0n nh\u01b0 m\u1ed9t tr\u00ecnh tr\u00edch xu\u1ea5t t\u00ednh n\u0103ng<\/td>\n<\/tr>\n<tr>\n<td>Chuy\u1ec3n ti\u1ebfp h\u1ecdc t\u1eadp<\/td>\n<td>T\u00e1i s\u1eed d\u1ee5ng ki\u1ebfn tr\u00fac ho\u1eb7c tr\u1ecdng l\u01b0\u1ee3ng c\u1ee7a m\u00f4 h\u00ecnh \u0111\u01b0\u1ee3c \u0111\u00e0o t\u1ea1o tr\u01b0\u1edbc<\/td>\n<\/tr>\n<tr>\n<td>Tinh ch\u1ec9nh<\/td>\n<td>Ti\u1ebfp t\u1ee5c \u0111\u00e0o t\u1ea1o m\u00f4 h\u00ecnh \u0111\u01b0\u1ee3c \u0111\u00e0o t\u1ea1o tr\u01b0\u1edbc v\u1ec1 nhi\u1ec7m v\u1ee5 m\u1edbi<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<h2>Quan \u0111i\u1ec3m t\u01b0\u01a1ng lai v\u00e0 c\u00f4ng ngh\u1ec7 m\u1edbi n\u1ed5i<\/h2>\n<p>T\u01b0\u01a1ng lai c\u1ee7a vi\u1ec7c tinh ch\u1ec9nh n\u1eb1m \u1edf nh\u1eefng c\u00e1ch hi\u1ec7u qu\u1ea3 v\u00e0 hi\u1ec7u qu\u1ea3 h\u01a1n \u0111\u1ec3 chuy\u1ec3n giao ki\u1ebfn th\u1ee9c gi\u1eefa c\u00e1c nhi\u1ec7m v\u1ee5. C\u00e1c k\u1ef9 thu\u1eadt m\u1edbi \u0111ang \u0111\u01b0\u1ee3c ph\u00e1t tri\u1ec3n \u0111\u1ec3 gi\u1ea3i quy\u1ebft c\u00e1c v\u1ea5n \u0111\u1ec1 nh\u01b0 qu\u00ean th\u1ea3m kh\u1ed1c v\u00e0 chuy\u1ec3n giao ti\u00eau c\u1ef1c, ch\u1eb3ng h\u1ea1n nh\u01b0 H\u1ee3p nh\u1ea5t tr\u1ecdng l\u01b0\u1ee3ng \u0111\u00e0n h\u1ed3i v\u00e0 M\u1ea1ng l\u01b0\u1edbi th\u1ea7n kinh ti\u1ebfn b\u1ed9. H\u01a1n n\u1eefa, vi\u1ec7c tinh ch\u1ec9nh d\u1ef1 ki\u1ebfn s\u1ebd \u0111\u00f3ng m\u1ed9t vai tr\u00f2 then ch\u1ed1t trong vi\u1ec7c ph\u00e1t tri\u1ec3n c\u00e1c m\u00f4 h\u00ecnh AI m\u1ea1nh m\u1ebd v\u00e0 hi\u1ec7u qu\u1ea3 h\u01a1n.<\/p>\n<h2>Tinh ch\u1ec9nh v\u00e0 m\u00e1y ch\u1ee7 proxy<\/h2>\n<p>M\u1eb7c d\u00f9 tinh ch\u1ec9nh li\u00ean quan tr\u1ef1c ti\u1ebfp h\u01a1n \u0111\u1ebfn h\u1ecdc m\u00e1y nh\u01b0ng n\u00f3 c\u00f3 li\u00ean quan m\u1eadt thi\u1ebft \u0111\u1ebfn m\u00e1y ch\u1ee7 proxy. M\u00e1y ch\u1ee7 proxy th\u01b0\u1eddng s\u1eed d\u1ee5ng c\u00e1c m\u00f4 h\u00ecnh h\u1ecdc m\u00e1y cho c\u00e1c t\u00e1c v\u1ee5 nh\u01b0 l\u1ecdc l\u01b0u l\u01b0\u1ee3ng, ph\u00e1t hi\u1ec7n m\u1ed1i \u0111e d\u1ecda v\u00e0 n\u00e9n d\u1eef li\u1ec7u. Vi\u1ec7c tinh ch\u1ec9nh c\u00f3 th\u1ec3 cho ph\u00e9p c\u00e1c m\u00f4 h\u00ecnh n\u00e0y th\u00edch \u1ee9ng t\u1ed1t h\u01a1n v\u1edbi c\u00e1c ki\u1ec3u l\u01b0u l\u01b0\u1ee3ng truy c\u1eadp ri\u00eang v\u00e0 b\u1ed1i c\u1ea3nh m\u1ed1i \u0111e d\u1ecda c\u1ee7a c\u00e1c m\u1ea1ng kh\u00e1c nhau, c\u1ea3i thi\u1ec7n hi\u1ec7u su\u1ea5t v\u00e0 t\u00ednh b\u1ea3o m\u1eadt t\u1ed5ng th\u1ec3 c\u1ee7a m\u00e1y ch\u1ee7 proxy.<\/p>\n<h2>Li\u00ean k\u1ebft li\u00ean quan<\/h2>\n<ul>\n<li><a href=\"https:\/\/www.nature.com\/articles\/s41598-019-52380-8\" target=\"_new\" rel=\"noopener nofollow\">Hi\u1ec3u v\u1ec1 h\u1ecdc chuy\u1ec3n giao cho h\u00ecnh \u1ea3nh y t\u1ebf<\/a><\/li>\n<li><a href=\"https:\/\/www.tensorflow.org\/tutorials\/images\/transfer_learning\" target=\"_new\" rel=\"noopener nofollow\">Tinh ch\u1ec9nh c\u00e1c m\u00f4 h\u00ecnh \u0111\u01b0\u1ee3c \u0111\u00e0o t\u1ea1o tr\u01b0\u1edbc<\/a><\/li>\n<li><a href=\"https:\/\/www.cloudflare.com\/learning\/cdn\/glossary\/reverse-proxy\/\" target=\"_new\" rel=\"noopener nofollow\">T\u1ed5ng quan v\u1ec1 m\u00e1y ch\u1ee7 proxy<\/a><\/li>\n<\/ul>","protected":false},"featured_media":491207,"menu_order":0,"template":"","meta":{"_acf_changed":false,"content-type":"","inline_featured_image":false,"footnotes":""},"class_list":["post-477239","wiki","type-wiki","status-publish","has-post-thumbnail","hentry"],"acf":{"faq_title":"Frequently Asked Questions about <mark>Fine-Tuning: A Detailed Overview<\/mark>","faq_items":[{"question":"What is Fine-Tuning in the context of machine learning?","answer":"Fine-tuning is a transfer learning technique in machine learning where a pre-trained model is adapted to suit a different, yet related, task. It leverages the pre-trained model's learned features, saving considerable time and computational resources compared to training a model from scratch."},{"question":"What is the history of Fine-tuning?","answer":"Fine-tuning, in the context of machine learning and deep learning, emerged from the concept of transfer learning. It became increasingly popular with the advent of deep learning and big data in the 2010s. The idea is to harness the power of an already trained model to train a new model for a different but related task."},{"question":"How does Fine-tuning work?","answer":"Fine-tuning is typically carried out in two stages. First, feature extraction where the pre-trained model is used as a fixed feature extractor. The output from this model is fed into a new model, which is then trained on the new task. Then, the fine-tuning stage, where specific layers of the model are \"unfrozen\" and the model is trained again on the new task, but with a very low learning rate."},{"question":"What are the key features of Fine-tuning?","answer":"The key features of fine-tuning include transfer of knowledge, computational efficiency, flexibility, and improved performance. It allows effective knowledge transfer from one task to another, is less computationally intensive, flexible in applying to different layers of the pre-trained model, and often leads to improved model performance."},{"question":"What are the types of Fine-tuning?","answer":"There are primarily two types of fine-tuning: Feature-based Fine-Tuning and Full Fine-Tuning. In the former, the pre-trained model is used as a fixed feature extractor while the new model is trained using these extracted features. In the latter, all or specific layers of the pre-trained model are unfrozen and trained on the new task."},{"question":"What are the applications and challenges of Fine-tuning?","answer":"Fine-tuning is used in various machine learning domains like computer vision, natural language processing, and audio processing. However, it can present challenges like Catastrophic Forgetting and Negative Transfer, which refer to the model forgetting the learned features from the base task while fine-tuning on the new task, and the base model's knowledge negatively impacting the performance on the new task, respectively."},{"question":"How does Fine-tuning compare with similar concepts like feature extraction and transfer learning?","answer":"While fine-tuning, feature extraction, and transfer learning are all related, they differ in their processes. Feature extraction uses the base model purely as a feature extractor without any further training. In contrast, fine-tuning continues the training process on the new task. Transfer learning is a broader term that can encompass both fine-tuning and feature extraction."},{"question":"What is the future perspective of Fine-tuning?","answer":"The future of fine-tuning lies in more efficient and effective ways to transfer knowledge between tasks. Emerging technologies are developing new techniques to address challenges like catastrophic forgetting and negative transfer. Fine-tuning is expected to play a pivotal role in the development of more robust and efficient AI models."},{"question":"How is Fine-tuning related to proxy servers?","answer":"Fine-tuning has relevance to proxy servers as these servers often employ machine learning models for tasks such as traffic filtering, threat detection, and data compression. Fine-tuning can enable these models to better adapt to the unique traffic patterns and threat landscapes of different networks, improving the overall performance and security of the proxy server."}]},"_links":{"self":[{"href":"https:\/\/oneproxy.pro\/vn\/wp-json\/wp\/v2\/wiki\/477239","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\/477239\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/oneproxy.pro\/vn\/wp-json\/wp\/v2\/media\/491207"}],"wp:attachment":[{"href":"https:\/\/oneproxy.pro\/vn\/wp-json\/wp\/v2\/media?parent=477239"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}