{"id":476437,"date":"2023-08-09T07:29:55","date_gmt":"2023-08-09T07:29:55","guid":{"rendered":""},"modified":"2023-09-05T11:12:44","modified_gmt":"2023-09-05T11:12:44","slug":"convolutional-neural-networks-cnn","status":"publish","type":"wiki","link":"https:\/\/oneproxy.pro\/vn\/wiki\/convolutional-neural-networks-cnn\/","title":{"rendered":"M\u1ea1ng th\u1ea7n kinh chuy\u1ec3n \u0111\u1ed5i (CNN)"},"content":{"rendered":"<p>M\u1ea1ng th\u1ea7n kinh chuy\u1ec3n \u0111\u1ed5i (CNN) l\u00e0 m\u1ed9t l\u1edbp thu\u1eadt to\u00e1n h\u1ecdc s\u00e2u \u0111\u00e3 c\u00e1ch m\u1ea1ng h\u00f3a l\u0129nh v\u1ef1c th\u1ecb gi\u00e1c m\u00e1y t\u00ednh v\u00e0 x\u1eed l\u00fd h\u00ecnh \u1ea3nh. Ch\u00fang l\u00e0 m\u1ed9t lo\u1ea1i m\u1ea1ng th\u1ea7n kinh nh\u00e2n t\u1ea1o chuy\u00ean d\u1ee5ng \u0111\u01b0\u1ee3c thi\u1ebft k\u1ebf \u0111\u1ec3 x\u1eed l\u00fd v\u00e0 nh\u1eadn d\u1ea1ng d\u1eef li\u1ec7u h\u00ecnh \u1ea3nh, gi\u00fap ch\u00fang c\u00f3 hi\u1ec7u qu\u1ea3 \u0111\u1eb7c bi\u1ec7t trong c\u00e1c nhi\u1ec7m v\u1ee5 nh\u01b0 ph\u00e2n lo\u1ea1i h\u00ecnh \u1ea3nh, ph\u00e1t hi\u1ec7n \u0111\u1ed1i t\u01b0\u1ee3ng v\u00e0 t\u1ea1o h\u00ecnh \u1ea3nh. \u00dd t\u01b0\u1edfng c\u1ed1t l\u00f5i \u0111\u1eb1ng sau CNN l\u00e0 b\u1eaft ch\u01b0\u1edbc qu\u00e1 tr\u00ecnh x\u1eed l\u00fd h\u00ecnh \u1ea3nh c\u1ee7a b\u1ed9 n\u00e3o con ng\u01b0\u1eddi, cho ph\u00e9p ch\u00fang t\u1ef1 \u0111\u1ed9ng t\u00ecm hi\u1ec3u v\u00e0 tr\u00edch xu\u1ea5t c\u00e1c m\u00f4 h\u00ecnh v\u00e0 \u0111\u1eb7c \u0111i\u1ec3m ph\u00e2n c\u1ea5p t\u1eeb h\u00ecnh \u1ea3nh.<\/p>\n<h2>L\u1ecbch s\u1eed ngu\u1ed3n g\u1ed1c c\u1ee7a M\u1ea1ng th\u1ea7n kinh chuy\u1ec3n \u0111\u1ed5i (CNN)<\/h2>\n<p>L\u1ecbch s\u1eed c\u1ee7a CNN c\u00f3 th\u1ec3 b\u1eaft ngu\u1ed3n t\u1eeb nh\u1eefng n\u0103m 1960, v\u1edbi s\u1ef1 ph\u00e1t tri\u1ec3n c\u1ee7a m\u1ea1ng l\u01b0\u1edbi th\u1ea7n kinh nh\u00e2n t\u1ea1o \u0111\u1ea7u ti\u00ean, \u0111\u01b0\u1ee3c g\u1ecdi l\u00e0 perceptron. Tuy nhi\u00ean, kh\u00e1i ni\u1ec7m m\u1ea1ng t\u00edch ch\u1eadp, n\u1ec1n t\u1ea3ng c\u1ee7a CNN, \u0111\u00e3 \u0111\u01b0\u1ee3c \u0111\u01b0a ra v\u00e0o nh\u1eefng n\u0103m 1980. N\u0103m 1989, Yann LeCun, c\u00f9ng v\u1edbi nh\u1eefng ng\u01b0\u1eddi kh\u00e1c, \u0111\u00e3 \u0111\u1ec1 xu\u1ea5t ki\u1ebfn tr\u00fac LeNet-5, \u0111\u00e2y l\u00e0 m\u1ed9t trong nh\u1eefng tri\u1ec3n khai th\u00e0nh c\u00f4ng s\u1edbm nh\u1ea5t c\u1ee7a CNN. M\u1ea1ng n\u00e0y ch\u1ee7 y\u1ebfu \u0111\u01b0\u1ee3c s\u1eed d\u1ee5ng \u0111\u1ec3 nh\u1eadn d\u1ea1ng ch\u1eef s\u1ed1 vi\u1ebft tay v\u00e0 \u0111\u1eb7t n\u1ec1n m\u00f3ng cho nh\u1eefng ti\u1ebfn b\u1ed9 trong x\u1eed l\u00fd h\u00ecnh \u1ea3nh trong t\u01b0\u01a1ng lai.<\/p>\n<h2>Th\u00f4ng tin chi ti\u1ebft v\u1ec1 M\u1ea1ng th\u1ea7n kinh chuy\u1ec3n \u0111\u1ed5i (CNN)<\/h2>\n<p>CNN \u0111\u01b0\u1ee3c l\u1ea5y c\u1ea3m h\u1ee9ng t\u1eeb h\u1ec7 th\u1ed1ng th\u1ecb gi\u00e1c c\u1ee7a con ng\u01b0\u1eddi, \u0111\u1eb7c bi\u1ec7t l\u00e0 t\u1ed5 ch\u1ee9c v\u1ecf n\u00e3o th\u1ecb gi\u00e1c. Ch\u00fang bao g\u1ed3m nhi\u1ec1u l\u1edbp, m\u1ed7i l\u1edbp \u0111\u01b0\u1ee3c thi\u1ebft k\u1ebf \u0111\u1ec3 th\u1ef1c hi\u1ec7n c\u00e1c thao t\u00e1c c\u1ee5 th\u1ec3 tr\u00ean d\u1eef li\u1ec7u \u0111\u1ea7u v\u00e0o. C\u00e1c l\u1edbp ch\u00ednh trong ki\u1ebfn tr\u00fac CNN \u0111i\u1ec3n h\u00ecnh l\u00e0:<\/p>\n<ol>\n<li>\n<p><strong>L\u1edbp \u0111\u1ea7u v\u00e0o:<\/strong> L\u1edbp n\u00e0y nh\u1eadn d\u1eef li\u1ec7u h\u00ecnh \u1ea3nh th\u00f4 l\u00e0m \u0111\u1ea7u v\u00e0o.<\/p>\n<\/li>\n<li>\n<p><strong>L\u1edbp ch\u1eadp:<\/strong> L\u1edbp ch\u1eadp l\u00e0 tr\u00e1i tim c\u1ee7a CNN. N\u00f3 bao g\u1ed3m nhi\u1ec1u b\u1ed9 l\u1ecdc (c\u00f2n g\u1ecdi l\u00e0 h\u1ea1t nh\u00e2n) tr\u01b0\u1ee3t tr\u00ean h\u00ecnh \u1ea3nh \u0111\u1ea7u v\u00e0o, tr\u00edch xu\u1ea5t c\u00e1c \u0111\u1eb7c \u0111i\u1ec3m c\u1ee5c b\u1ed9 th\u00f4ng qua c\u00e1c ph\u00e9p t\u00edch ch\u1eadp. M\u1ed7i b\u1ed9 l\u1ecdc ch\u1ecbu tr\u00e1ch nhi\u1ec7m ph\u00e1t hi\u1ec7n c\u00e1c m\u1eabu c\u1ee5 th\u1ec3, nh\u01b0 c\u00e1c c\u1ea1nh ho\u1eb7c h\u1ecda ti\u1ebft.<\/p>\n<\/li>\n<li>\n<p><strong>Ch\u1ee9c n\u0103ng k\u00edch ho\u1ea1t:<\/strong> Sau thao t\u00e1c t\u00edch ch\u1eadp, m\u1ed9t h\u00e0m k\u00edch ho\u1ea1t (th\u01b0\u1eddng l\u00e0 ReLU - \u0110\u01a1n v\u1ecb tuy\u1ebfn t\u00ednh \u0111\u01b0\u1ee3c ch\u1ec9nh s\u1eeda) \u0111\u01b0\u1ee3c \u00e1p d\u1ee5ng theo t\u1eebng ph\u1ea7n t\u1eed \u0111\u1ec3 \u0111\u01b0a t\u00ednh phi tuy\u1ebfn t\u00ednh v\u00e0o m\u1ea1ng, cho ph\u00e9p m\u1ea1ng t\u00ecm hi\u1ec3u c\u00e1c m\u1eabu ph\u1ee9c t\u1ea1p h\u01a1n.<\/p>\n<\/li>\n<li>\n<p><strong>L\u1edbp g\u1ed9p:<\/strong> C\u00e1c l\u1edbp g\u1ed9p (th\u01b0\u1eddng l\u00e0 g\u1ed9p t\u1ed1i \u0111a) \u0111\u01b0\u1ee3c s\u1eed d\u1ee5ng \u0111\u1ec3 gi\u1ea3m k\u00edch th\u01b0\u1edbc kh\u00f4ng gian c\u1ee7a d\u1eef li\u1ec7u v\u00e0 gi\u1ea3m \u0111\u1ed9 ph\u1ee9c t\u1ea1p t\u00ednh to\u00e1n trong khi v\u1eabn gi\u1eef \u0111\u01b0\u1ee3c th\u00f4ng tin c\u1ea7n thi\u1ebft.<\/p>\n<\/li>\n<li>\n<p><strong>L\u1edbp \u0111\u01b0\u1ee3c k\u1ebft n\u1ed1i \u0111\u1ea7y \u0111\u1ee7:<\/strong> C\u00e1c l\u1edbp n\u00e0y k\u1ebft n\u1ed1i t\u1ea5t c\u1ea3 c\u00e1c n\u01a1-ron t\u1eeb l\u1edbp tr\u01b0\u1edbc v\u1edbi m\u1ecdi n\u01a1-ron trong l\u1edbp hi\u1ec7n t\u1ea1i. H\u1ecd t\u1ed5ng h\u1ee3p c\u00e1c \u0111\u1eb7c \u0111i\u1ec3m \u0111\u00e3 h\u1ecdc v\u00e0 \u0111\u01b0a ra quy\u1ebft \u0111\u1ecbnh cu\u1ed1i c\u00f9ng cho vi\u1ec7c ph\u00e2n lo\u1ea1i ho\u1eb7c c\u00e1c nhi\u1ec7m v\u1ee5 kh\u00e1c.<\/p>\n<\/li>\n<li>\n<p><strong>L\u1edbp \u0111\u1ea7u ra:<\/strong> L\u1edbp cu\u1ed1i c\u00f9ng t\u1ea1o ra \u0111\u1ea7u ra c\u1ee7a m\u1ea1ng, c\u00f3 th\u1ec3 l\u00e0 nh\u00e3n l\u1edbp \u0111\u1ec3 ph\u00e2n lo\u1ea1i h\u00ecnh \u1ea3nh ho\u1eb7c m\u1ed9t t\u1eadp h\u1ee3p c\u00e1c tham s\u1ed1 \u0111\u1ec3 t\u1ea1o h\u00ecnh \u1ea3nh.<\/p>\n<\/li>\n<\/ol>\n<h2>C\u1ea5u tr\u00fac b\u00ean trong c\u1ee7a M\u1ea1ng th\u1ea7n kinh t\u00edch ch\u1eadp (CNN)<\/h2>\n<p>C\u1ea5u tr\u00fac b\u00ean trong c\u1ee7a CNN tu\u00e2n theo c\u01a1 ch\u1ebf chuy\u1ec3n ti\u1ebfp ngu\u1ed3n c\u1ea5p d\u1eef li\u1ec7u. Khi m\u1ed9t h\u00ecnh \u1ea3nh \u0111\u01b0\u1ee3c \u0111\u01b0a v\u00e0o m\u1ea1ng, n\u00f3 s\u1ebd \u0111i qua t\u1eebng l\u1edbp m\u1ed9t c\u00e1ch tu\u1ea7n t\u1ef1, v\u1edbi tr\u1ecdng s\u1ed1 v\u00e0 \u0111\u1ed9 l\u1ec7ch \u0111\u01b0\u1ee3c \u0111i\u1ec1u ch\u1ec9nh trong qu\u00e1 tr\u00ecnh hu\u1ea5n luy\u1ec7n th\u00f4ng qua lan truy\u1ec1n ng\u01b0\u1ee3c. Vi\u1ec7c t\u1ed1i \u01b0u h\u00f3a l\u1eb7p \u0111i l\u1eb7p l\u1ea1i n\u00e0y gi\u00fap m\u1ea1ng h\u1ecdc c\u00e1ch nh\u1eadn bi\u1ebft v\u00e0 ph\u00e2n bi\u1ec7t gi\u1eefa c\u00e1c t\u00ednh n\u0103ng v\u00e0 \u0111\u1ed1i t\u01b0\u1ee3ng kh\u00e1c nhau trong h\u00ecnh \u1ea3nh.<\/p>\n<h2>Ph\u00e2n t\u00edch c\u00e1c t\u00ednh n\u0103ng ch\u00ednh c\u1ee7a M\u1ea1ng th\u1ea7n kinh chuy\u1ec3n \u0111\u1ed5i (CNN)<\/h2>\n<p>CNN s\u1edf h\u1eefu m\u1ed9t s\u1ed1 t\u00ednh n\u0103ng ch\u00ednh gi\u00fap ch\u00fang c\u00f3 hi\u1ec7u qu\u1ea3 cao trong vi\u1ec7c ph\u00e2n t\u00edch d\u1eef li\u1ec7u tr\u1ef1c quan:<\/p>\n<ol>\n<li>\n<p><strong>T\u00ednh n\u0103ng h\u1ecdc t\u1eadp:<\/strong> CNN t\u1ef1 \u0111\u1ed9ng t\u00ecm hi\u1ec3u c\u00e1c t\u00ednh n\u0103ng ph\u00e2n c\u1ea5p t\u1eeb d\u1eef li\u1ec7u th\u00f4, lo\u1ea1i b\u1ecf nhu c\u1ea7u k\u1ef9 thu\u1eadt t\u00ednh n\u0103ng th\u1ee7 c\u00f4ng.<\/p>\n<\/li>\n<li>\n<p><strong>D\u1ecbch b\u1ea5t bi\u1ebfn:<\/strong> C\u00e1c l\u1edbp t\u00edch ch\u1eadp cho ph\u00e9p CNN ph\u00e1t hi\u1ec7n c\u00e1c m\u1eabu b\u1ea5t k\u1ec3 v\u1ecb tr\u00ed c\u1ee7a ch\u00fang trong \u1ea3nh, cung c\u1ea5p t\u00ednh b\u1ea5t bi\u1ebfn d\u1ecbch thu\u1eadt.<\/p>\n<\/li>\n<li>\n<p><strong>Chia s\u1ebb tham s\u1ed1:<\/strong> Vi\u1ec7c chia s\u1ebb tr\u1ecdng s\u1ed1 gi\u1eefa c\u00e1c v\u1ecb tr\u00ed kh\u00f4ng gian gi\u00fap gi\u1ea3m s\u1ed1 l\u01b0\u1ee3ng tham s\u1ed1, gi\u00fap CNN ho\u1ea1t \u0111\u1ed9ng hi\u1ec7u qu\u1ea3 h\u01a1n v\u00e0 c\u00f3 kh\u1ea3 n\u0103ng m\u1edf r\u1ed9ng h\u01a1n.<\/p>\n<\/li>\n<li>\n<p><strong>T\u1eadp h\u1ee3p c\u00e1c h\u1ec7 th\u1ed1ng ph\u00e2n c\u1ea5p kh\u00f4ng gian:<\/strong> C\u00e1c l\u1edbp g\u1ed9p d\u1ea7n d\u1ea7n gi\u1ea3m k\u00edch th\u01b0\u1edbc kh\u00f4ng gian, cho ph\u00e9p m\u1ea1ng nh\u1eadn d\u1ea1ng c\u00e1c t\u00ednh n\u0103ng \u1edf c\u00e1c t\u1ef7 l\u1ec7 kh\u00e1c nhau.<\/p>\n<\/li>\n<li>\n<p><strong>Ki\u1ebfn tr\u00fac s\u00e2u:<\/strong> CNN c\u00f3 th\u1ec3 s\u00e2u, c\u00f3 nhi\u1ec1u l\u1edbp, cho ph\u00e9p ch\u00fang t\u00ecm hi\u1ec3u c\u00e1c c\u00e1ch bi\u1ec3u di\u1ec5n ph\u1ee9c t\u1ea1p v\u00e0 tr\u1eebu t\u01b0\u1ee3ng.<\/p>\n<\/li>\n<\/ol>\n<h2>C\u00e1c lo\u1ea1i m\u1ea1ng th\u1ea7n kinh chuy\u1ec3n \u0111\u1ed5i (CNN)<\/h2>\n<p>CNN c\u00f3 nhi\u1ec1u ki\u1ebfn tr\u00fac kh\u00e1c nhau, m\u1ed7i ki\u1ebfn tr\u00fac \u0111\u01b0\u1ee3c thi\u1ebft k\u1ebf ri\u00eang cho c\u00e1c nhi\u1ec7m v\u1ee5 c\u1ee5 th\u1ec3. M\u1ed9t s\u1ed1 ki\u1ebfn tr\u00fac CNN ph\u1ed5 bi\u1ebfn bao g\u1ed3m:<\/p>\n<ol>\n<li>\n<p><strong>LeNet-5:<\/strong> M\u1ed9t trong nh\u1eefng CNN \u0111\u1ea7u ti\u00ean \u0111\u01b0\u1ee3c thi\u1ebft k\u1ebf \u0111\u1ec3 nh\u1eadn d\u1ea1ng ch\u1eef s\u1ed1 vi\u1ebft tay.<\/p>\n<\/li>\n<li>\n<p><strong>AlexNet:<\/strong> \u0110\u01b0\u1ee3c gi\u1edbi thi\u1ec7u v\u00e0o n\u0103m 2012, \u0111\u00e2y l\u00e0 CNN s\u00e2u \u0111\u1ea7u ti\u00ean gi\u00e0nh chi\u1ebfn th\u1eafng trong Th\u1eed th\u00e1ch nh\u1eadn d\u1ea1ng h\u00ecnh \u1ea3nh quy m\u00f4 l\u1edbn ImageNet (ILSVRC).<\/p>\n<\/li>\n<li>\n<p><strong>VGGNet:<\/strong> \u0110\u01b0\u1ee3c bi\u1ebft \u0111\u1ebfn nh\u1edd s\u1ef1 \u0111\u01a1n gi\u1ea3n v\u1edbi ki\u1ebfn tr\u00fac th\u1ed1ng nh\u1ea5t, s\u1eed d\u1ee5ng c\u00e1c b\u1ed9 l\u1ecdc t\u00edch ch\u1eadp 3\u00d73 tr\u00ean to\u00e0n m\u1ea1ng.<\/p>\n<\/li>\n<li>\n<p><strong>ResNet:<\/strong> Gi\u1edbi thi\u1ec7u b\u1ecf qua c\u00e1c k\u1ebft n\u1ed1i (kh\u1ed1i d\u01b0) \u0111\u1ec3 gi\u1ea3i quy\u1ebft c\u00e1c v\u1ea5n \u0111\u1ec1 v\u1ec1 \u0111\u1ed9 d\u1ed1c bi\u1ebfn m\u1ea5t trong c\u00e1c m\u1ea1ng r\u1ea5t s\u00e2u.<\/p>\n<\/li>\n<li>\n<p><strong>Kh\u1edfi \u0111\u1ea7u (GoogleNet):<\/strong> S\u1eed d\u1ee5ng c\u00e1c m\u00f4-\u0111un kh\u1edfi \u0111\u1ed9ng v\u1edbi c\u00e1c c\u1ea5u tr\u00fac song song c\u00f3 k\u00edch th\u01b0\u1edbc kh\u00e1c nhau \u0111\u1ec3 n\u1eafm b\u1eaft c\u00e1c t\u00ednh n\u0103ng \u0111a quy m\u00f4.<\/p>\n<\/li>\n<li>\n<p><strong>MobileNet:<\/strong> T\u1ed1i \u01b0u h\u00f3a cho thi\u1ebft b\u1ecb di \u0111\u1ed9ng v\u00e0 thi\u1ebft b\u1ecb nh\u00fang, t\u1ea1o s\u1ef1 c\u00e2n b\u1eb1ng gi\u1eefa \u0111\u1ed9 ch\u00ednh x\u00e1c v\u00e0 hi\u1ec7u qu\u1ea3 t\u00ednh to\u00e1n.<\/p>\n<\/li>\n<\/ol>\n<p>B\u1ea3ng: C\u00e1c ki\u1ebfn tr\u00fac CNN ph\u1ed5 bi\u1ebfn v\u00e0 \u1ee9ng d\u1ee5ng c\u1ee7a ch\u00fang<\/p>\n<table>\n<thead>\n<tr>\n<th>Ng\u00e0nh ki\u1ebfn tr\u00fac<\/th>\n<th>C\u00e1c \u1ee9ng d\u1ee5ng<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td>LeNet-5<\/td>\n<td>Nh\u1eadn d\u1ea1ng ch\u1eef s\u1ed1 vi\u1ebft tay<\/td>\n<\/tr>\n<tr>\n<td>AlexNet<\/td>\n<td>Ph\u00e2n lo\u1ea1i h\u00ecnh \u1ea3nh<\/td>\n<\/tr>\n<tr>\n<td>VGGNet<\/td>\n<td>Nh\u1eadn d\u1ea1ng \u0111\u1ed1i t\u01b0\u1ee3ng<\/td>\n<\/tr>\n<tr>\n<td>ResNet<\/td>\n<td>Deep Learning trong c\u00e1c nhi\u1ec7m v\u1ee5 kh\u00e1c nhau<\/td>\n<\/tr>\n<tr>\n<td>Kh\u1edfi \u0111\u1ea7u<\/td>\n<td>Nh\u1eadn d\u1ea1ng v\u00e0 ph\u00e2n \u0111o\u1ea1n h\u00ecnh \u1ea3nh<\/td>\n<\/tr>\n<tr>\n<td>M\u1ea1ng di \u0111\u1ed9ng<\/td>\n<td>T\u1ea7m nh\u00ecn thi\u1ebft b\u1ecb di \u0111\u1ed9ng v\u00e0 nh\u00fang<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<h2>C\u00e1c c\u00e1ch s\u1eed d\u1ee5ng M\u1ea1ng th\u1ea7n kinh chuy\u1ec3n \u0111\u1ed5i (CNN), c\u00e1c v\u1ea5n \u0111\u1ec1 v\u00e0 gi\u1ea3i ph\u00e1p<\/h2>\n<p>C\u00e1c \u1ee9ng d\u1ee5ng c\u1ee7a CNN r\u1ea5t r\u1ed9ng l\u1edbn v\u00e0 kh\u00f4ng ng\u1eebng m\u1edf r\u1ed9ng. M\u1ed9t s\u1ed1 tr\u01b0\u1eddng h\u1ee3p s\u1eed d\u1ee5ng ph\u1ed5 bi\u1ebfn bao g\u1ed3m:<\/p>\n<ol>\n<li>\n<p><strong>Ph\u00e2n lo\u1ea1i h\u00ecnh \u1ea3nh:<\/strong> G\u00e1n nh\u00e3n cho h\u00ecnh \u1ea3nh d\u1ef1a tr\u00ean n\u1ed9i dung c\u1ee7a ch\u00fang.<\/p>\n<\/li>\n<li>\n<p><strong>Ph\u00e1t hi\u1ec7n \u0111\u1ed1i t\u01b0\u1ee3ng:<\/strong> Nh\u1eadn d\u1ea1ng v\u00e0 \u0111\u1ecbnh v\u1ecb c\u00e1c \u0111\u1ed1i t\u01b0\u1ee3ng trong \u1ea3nh.<\/p>\n<\/li>\n<li>\n<p><strong>Ph\u00e2n \u0111o\u1ea1n ng\u1eef ngh\u0129a:<\/strong> G\u00e1n nh\u00e3n l\u1edbp cho t\u1eebng pixel trong \u1ea3nh.<\/p>\n<\/li>\n<li>\n<p><strong>T\u1ea1o h\u00ecnh \u1ea3nh:<\/strong> T\u1ea1o h\u00ecnh \u1ea3nh m\u1edbi t\u1eeb \u0111\u1ea7u, ch\u1eb3ng h\u1ea1n nh\u01b0 chuy\u1ec3n ki\u1ec3u ho\u1eb7c GAN (M\u1ea1ng \u0111\u1ed1i th\u1ee7 s\u00e1ng t\u1ea1o).<\/p>\n<\/li>\n<\/ol>\n<p>M\u1eb7c d\u00f9 th\u00e0nh c\u00f4ng nh\u01b0ng CNN v\u1eabn ph\u1ea3i \u0111\u1ed1i m\u1eb7t v\u1edbi nh\u1eefng th\u00e1ch th\u1ee9c nh\u01b0:<\/p>\n<ol>\n<li>\n<p><strong>Trang b\u1ecb qu\u00e1 m\u1ee9c:<\/strong> X\u1ea3y ra khi m\u00f4 h\u00ecnh ho\u1ea1t \u0111\u1ed9ng t\u1ed1t tr\u00ean d\u1eef li\u1ec7u hu\u1ea5n luy\u1ec7n nh\u01b0ng k\u00e9m tr\u00ean d\u1eef li\u1ec7u kh\u00f4ng nh\u00ecn th\u1ea5y.<\/p>\n<\/li>\n<li>\n<p><strong>C\u01b0\u1eddng \u0111\u1ed9 t\u00ednh to\u00e1n:<\/strong> CNN s\u00e2u y\u00eau c\u1ea7u t\u00e0i nguy\u00ean t\u00ednh to\u00e1n \u0111\u00e1ng k\u1ec3, h\u1ea1n ch\u1ebf vi\u1ec7c s\u1eed d\u1ee5ng ch\u00fang tr\u00ean m\u1ed9t s\u1ed1 thi\u1ebft b\u1ecb nh\u1ea5t \u0111\u1ecbnh.<\/p>\n<\/li>\n<\/ol>\n<p>\u0110\u1ec3 gi\u1ea3i quy\u1ebft nh\u1eefng v\u1ea5n \u0111\u1ec1 n\u00e0y, c\u00e1c k\u1ef9 thu\u1eadt nh\u01b0 t\u0103ng c\u01b0\u1eddng d\u1eef li\u1ec7u, ch\u00ednh quy h\u00f3a v\u00e0 n\u00e9n m\u00f4 h\u00ecnh th\u01b0\u1eddng \u0111\u01b0\u1ee3c s\u1eed d\u1ee5ng.<\/p>\n<h2>\u0110\u1eb7c \u0111i\u1ec3m ch\u00ednh v\u00e0 nh\u1eefng so s\u00e1nh kh\u00e1c<\/h2>\n<p>B\u1ea3ng: CNN so v\u1edbi M\u1ea1ng th\u1ea7n kinh truy\u1ec1n th\u1ed1ng<\/p>\n<table>\n<thead>\n<tr>\n<th>\u0110\u1eb7c tr\u01b0ng<\/th>\n<th>CNN<\/th>\n<th>NN truy\u1ec1n th\u1ed1ng<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td>\u0110\u1ea7u v\u00e0o<\/td>\n<td>Ch\u1ee7 y\u1ebfu \u0111\u01b0\u1ee3c s\u1eed d\u1ee5ng cho d\u1eef li\u1ec7u tr\u1ef1c quan<\/td>\n<td>Th\u00edch h\u1ee3p cho d\u1eef li\u1ec7u d\u1ea1ng b\u1ea3ng ho\u1eb7c tu\u1ea7n t\u1ef1<\/td>\n<\/tr>\n<tr>\n<td>Ng\u00e0nh ki\u1ebfn tr\u00fac<\/td>\n<td>Chuy\u00ean d\u00f9ng cho c\u00e1c m\u1eabu ph\u00e2n c\u1ea5p<\/td>\n<td>C\u00e1c l\u1edbp \u0111\u01a1n gi\u1ea3n, d\u00e0y \u0111\u1eb7c<\/td>\n<\/tr>\n<tr>\n<td>K\u1ef9 thu\u1eadt t\u00ednh n\u0103ng<\/td>\n<td>T\u1ef1 \u0111\u1ed9ng h\u1ecdc t\u00ednh n\u0103ng<\/td>\n<td>Y\u00eau c\u1ea7u k\u1ef9 thu\u1eadt t\u00ednh n\u0103ng th\u1ee7 c\u00f4ng<\/td>\n<\/tr>\n<tr>\n<td>D\u1ecbch b\u1ea5t bi\u1ebfn<\/td>\n<td>\u0110\u00fang<\/td>\n<td>KH\u00d4NG<\/td>\n<\/tr>\n<tr>\n<td>Chia s\u1ebb th\u00f4ng s\u1ed1<\/td>\n<td>\u0110\u00fang<\/td>\n<td>KH\u00d4NG<\/td>\n<\/tr>\n<tr>\n<td>H\u1ec7 th\u1ed1ng ph\u00e2n c\u1ea5p kh\u00f4ng gian<\/td>\n<td>S\u1eed d\u1ee5ng c\u00e1c l\u1edbp t\u1ed5ng h\u1ee3p<\/td>\n<td>Kh\u00f4ng \u00e1p d\u1ee5ng<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<h2>Quan \u0111i\u1ec3m v\u00e0 c\u00f4ng ngh\u1ec7 t\u01b0\u01a1ng lai li\u00ean quan \u0111\u1ebfn CNN<\/h2>\n<p>CNN \u0111\u00e3 t\u1ea1o ra t\u00e1c \u0111\u1ed9ng s\u00e2u s\u1eafc tr\u00ean nhi\u1ec1u ng\u00e0nh v\u00e0 l\u0129nh v\u1ef1c kh\u00e1c nhau, nh\u01b0ng ti\u1ec1m n\u0103ng c\u1ee7a ch\u00fang v\u1eabn ch\u01b0a c\u1ea1n ki\u1ec7t. M\u1ed9t s\u1ed1 quan \u0111i\u1ec3m v\u00e0 c\u00f4ng ngh\u1ec7 trong t\u01b0\u01a1ng lai li\u00ean quan \u0111\u1ebfn CNN bao g\u1ed3m:<\/p>\n<ol>\n<li>\n<p><strong>\u1ee8ng d\u1ee5ng th\u1eddi gian th\u1ef1c:<\/strong> Nghi\u00ean c\u1ee9u \u0111ang ti\u1ebfn h\u00e0nh t\u1eadp trung v\u00e0o vi\u1ec7c gi\u1ea3m y\u00eau c\u1ea7u t\u00ednh to\u00e1n, cho ph\u00e9p \u1ee9ng d\u1ee5ng th\u1eddi gian th\u1ef1c tr\u00ean c\u00e1c thi\u1ebft b\u1ecb c\u00f3 ngu\u1ed3n l\u1ef1c h\u1ea1n ch\u1ebf.<\/p>\n<\/li>\n<li>\n<p><strong>Kh\u1ea3 n\u0103ng gi\u1ea3i th\u00edch:<\/strong> Nh\u1eefng n\u1ed7 l\u1ef1c \u0111ang \u0111\u01b0\u1ee3c th\u1ef1c hi\u1ec7n \u0111\u1ec3 l\u00e0m cho CNN d\u1ec5 hi\u1ec3u h\u01a1n, cho ph\u00e9p ng\u01b0\u1eddi d\u00f9ng hi\u1ec3u \u0111\u01b0\u1ee3c c\u00e1c quy\u1ebft \u0111\u1ecbnh c\u1ee7a m\u00f4 h\u00ecnh.<\/p>\n<\/li>\n<li>\n<p><strong>Chuy\u1ec3n ti\u1ebfp h\u1ecdc t\u1eadp:<\/strong> C\u00e1c m\u00f4 h\u00ecnh CNN \u0111\u01b0\u1ee3c \u0111\u00e0o t\u1ea1o tr\u01b0\u1edbc c\u00f3 th\u1ec3 \u0111\u01b0\u1ee3c tinh ch\u1ec9nh cho c\u00e1c nhi\u1ec7m v\u1ee5 c\u1ee5 th\u1ec3, gi\u1ea3m nhu c\u1ea7u v\u1ec1 d\u1eef li\u1ec7u \u0111\u00e0o t\u1ea1o m\u1edf r\u1ed9ng.<\/p>\n<\/li>\n<li>\n<p><strong>H\u1ecdc t\u1eadp li\u00ean t\u1ee5c:<\/strong> T\u0103ng c\u01b0\u1eddng CNN \u0111\u1ec3 h\u1ecdc li\u00ean t\u1ee5c t\u1eeb d\u1eef li\u1ec7u m\u1edbi m\u00e0 kh\u00f4ng qu\u00ean th\u00f4ng tin \u0111\u00e3 h\u1ecdc tr\u01b0\u1edbc \u0111\u00f3.<\/p>\n<\/li>\n<\/ol>\n<h2>C\u00e1ch s\u1eed d\u1ee5ng ho\u1eb7c li\u00ean k\u1ebft m\u00e1y ch\u1ee7 proxy v\u1edbi M\u1ea1ng th\u1ea7n kinh chuy\u1ec3n \u0111\u1ed5i (CNN)<\/h2>\n<p>M\u00e1y ch\u1ee7 proxy \u0111\u00f3ng vai tr\u00f2 trung gian gi\u1eefa m\u00e1y kh\u00e1ch v\u00e0 internet, cung c\u1ea5p kh\u1ea3 n\u0103ng \u1ea9n danh, b\u1ea3o m\u1eadt v\u00e0 b\u1ed9 nh\u1edb \u0111\u1ec7m. Khi s\u1eed d\u1ee5ng CNN trong c\u00e1c \u1ee9ng d\u1ee5ng y\u00eau c\u1ea7u truy xu\u1ea5t d\u1eef li\u1ec7u t\u1eeb web, m\u00e1y ch\u1ee7 proxy c\u00f3 th\u1ec3:<\/p>\n<ol>\n<li>\n<p><strong>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 c\u00e1c y\u00eau c\u1ea7u v\u00e0 thu th\u1eadp b\u1ed9 d\u1eef li\u1ec7u h\u00ecnh \u1ea3nh \u0111\u1ec3 \u0111\u00e0o t\u1ea1o CNN.<\/p>\n<\/li>\n<li>\n<p><strong>B\u1ea3o v\u1ec7 quy\u1ec1n ri\u00eang t\u01b0:<\/strong> B\u1eb1ng c\u00e1ch \u0111\u1ecbnh tuy\u1ebfn c\u00e1c y\u00eau c\u1ea7u th\u00f4ng qua proxy, ng\u01b0\u1eddi d\u00f9ng c\u00f3 th\u1ec3 b\u1ea3o v\u1ec7 danh t\u00ednh v\u00e0 th\u00f4ng tin nh\u1ea1y c\u1ea3m c\u1ee7a m\u00ecnh trong qu\u00e1 tr\u00ecnh \u0111\u00e0o t\u1ea1o m\u00f4 h\u00ecnh.<\/p>\n<\/li>\n<li>\n<p><strong>C\u00e2n b\u1eb1ng t\u1ea3i:<\/strong> M\u00e1y ch\u1ee7 proxy c\u00f3 th\u1ec3 ph\u00e2n ph\u1ed1i c\u00e1c y\u00eau c\u1ea7u d\u1eef li\u1ec7u \u0111\u1ebfn tr\u00ean nhi\u1ec1u m\u00e1y ch\u1ee7 CNN, t\u1ed1i \u01b0u h\u00f3a vi\u1ec7c s\u1eed d\u1ee5ng t\u00e0i nguy\u00ean.<\/p>\n<\/li>\n<\/ol>\n<h2>Li\u00ean k\u1ebft li\u00ean quan<\/h2>\n<p>\u0110\u1ec3 bi\u1ebft th\u00eam th\u00f4ng tin v\u1ec1 M\u1ea1ng th\u1ea7n kinh chuy\u1ec3n \u0111\u1ed5i (CNN), b\u1ea1n c\u00f3 th\u1ec3 kh\u00e1m ph\u00e1 c\u00e1c t\u00e0i nguy\u00ean sau:<\/p>\n<ul>\n<li><a href=\"https:\/\/www.deeplearningbook.org\/contents\/convnets.html\" target=\"_new\" rel=\"noopener nofollow\">S\u00e1ch Deep Learning: Ch\u01b0\u01a1ng 9 \u2013 M\u1ea1ng t\u00edch ch\u1eadp<\/a><\/li>\n<li><a href=\"http:\/\/cs231n.stanford.edu\/\" target=\"_new\" rel=\"noopener nofollow\">Stanford CS231n - M\u1ea1ng th\u1ea7n kinh t\u00edch ch\u1eadp \u0111\u1ec3 nh\u1eadn d\u1ea1ng h\u00ecnh \u1ea3nh<\/a><\/li>\n<li><a href=\"https:\/\/towardsdatascience.com\/introduction-to-convolutional-neural-networks-cnn-with-tensorflow-57e2f4837e18\" target=\"_new\" rel=\"noopener nofollow\">H\u01b0\u1edbng t\u1edbi khoa h\u1ecdc d\u1eef li\u1ec7u - Gi\u1edbi thi\u1ec7u v\u1ec1 M\u1ea1ng th\u1ea7n kinh chuy\u1ec3n \u0111\u1ed5i<\/a><\/li>\n<\/ul>\n<p>V\u1edbi kh\u1ea3 n\u0103ng tr\u00edch xu\u1ea5t c\u00e1c m\u1eabu ph\u1ee9c t\u1ea1p t\u1eeb d\u1eef li\u1ec7u tr\u1ef1c quan, M\u1ea1ng th\u1ea7n kinh chuy\u1ec3n \u0111\u1ed5i ti\u1ebfp t\u1ee5c n\u00e2ng cao l\u0129nh v\u1ef1c th\u1ecb gi\u00e1c m\u00e1y t\u00ednh v\u00e0 v\u01b0\u1ee3t qua c\u00e1c ranh gi\u1edbi c\u1ee7a tr\u00ed tu\u1ec7 nh\u00e2n t\u1ea1o. Khi c\u00f4ng ngh\u1ec7 ph\u00e1t tri\u1ec3n v\u00e0 tr\u1edf n\u00ean d\u1ec5 ti\u1ebfp c\u1eadn h\u01a1n, ch\u00fang ta c\u00f3 th\u1ec3 mong \u0111\u1ee3i \u0111\u01b0\u1ee3c th\u1ea5y CNN \u0111\u01b0\u1ee3c t\u00edch h\u1ee3p v\u00e0o nhi\u1ec1u \u1ee9ng d\u1ee5ng, n\u00e2ng cao cu\u1ed9c s\u1ed1ng c\u1ee7a ch\u00fang ta theo nhi\u1ec1u c\u00e1ch.<\/p>","protected":false},"featured_media":468019,"menu_order":0,"template":"","meta":{"_acf_changed":false,"content-type":"","inline_featured_image":false,"footnotes":""},"class_list":["post-476437","wiki","type-wiki","status-publish","has-post-thumbnail","hentry"],"acf":{"faq_title":"Frequently Asked Questions about <mark>Convolutional Neural Networks (CNN)<\/mark>","faq_items":[{"question":"What are Convolutional Neural Networks (CNN)?","answer":"<p>Convolutional Neural Networks (CNN) are a type of deep learning algorithm designed for computer vision tasks, such as image classification, object detection, and image generation. They mimic the human visual system, automatically learning hierarchical patterns and features from images.<\/p>"},{"question":"How do Convolutional Neural Networks (CNN) work?","answer":"<p>CNNs consist of multiple layers, including convolutional layers, activation functions, pooling layers, and fully connected layers. The convolutional layers perform local feature extraction, activation functions introduce non-linearity, pooling layers reduce spatial dimensions, and fully connected layers make final decisions.<\/p>"},{"question":"What are the key features of Convolutional Neural Networks (CNN)?","answer":"<p>CNNs offer feature learning, translation invariance, parameter sharing, and the ability to capture spatial hierarchies. They automatically learn patterns, can detect objects regardless of their position, reduce the number of parameters, and recognize features at different scales.<\/p>"},{"question":"What types of Convolutional Neural Networks (CNN) exist?","answer":"<p>There are various CNN architectures, each tailored for specific tasks. Some popular ones include LeNet-5, AlexNet, VGGNet, ResNet, Inception, and MobileNet.<\/p>"},{"question":"What are the ways to use Convolutional Neural Networks (CNN)?","answer":"<p>CNNs find applications in image classification, object detection, semantic segmentation, and image generation. They can be used for numerous visual data analysis tasks.<\/p>"},{"question":"What problems do Convolutional Neural Networks (CNN) face?","answer":"<p>CNNs may encounter overfitting and require significant computational resources for deep networks. However, solutions such as data augmentation, regularization, and model compression can address these issues.<\/p>"},{"question":"How can proxy servers be associated with Convolutional Neural Networks (CNN)?","answer":"<p>Proxy servers can enhance CNN usage by anonymizing data collection requests, protecting privacy, and load balancing for efficient resource utilization.<\/p>"},{"question":"What is the future outlook for Convolutional Neural Networks (CNN)?","answer":"<p>CNNs continue to advance with real-time applications, improved explainability, transfer learning, and continual learning capabilities. Their potential impact spans across various industries.<\/p>"},{"question":"Where can I find more information about Convolutional Neural Networks (CNN)?","answer":"<p>For more in-depth knowledge, you can explore resources like the \"Deep Learning Book,\" Stanford CS231n, and Towards Data Science articles on CNNs. As a reliable proxy server provider, OneProxy brings you this comprehensive guide to CNNs and their applications.<\/p>"}]},"_links":{"self":[{"href":"https:\/\/oneproxy.pro\/vn\/wp-json\/wp\/v2\/wiki\/476437","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\/476437\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/oneproxy.pro\/vn\/wp-json\/wp\/v2\/media\/468019"}],"wp:attachment":[{"href":"https:\/\/oneproxy.pro\/vn\/wp-json\/wp\/v2\/media?parent=476437"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}