{"id":8613,"date":"2025-02-27T16:07:41","date_gmt":"2025-02-27T09:07:41","guid":{"rendered":"https:\/\/mb668s.com\/cam-nang-7mb66-xoc-dia\/?p=8613"},"modified":"2026-03-25T14:10:30","modified_gmt":"2026-03-25T07:10:30","slug":"convolutional-neural-network-la-gi","status":"publish","type":"post","link":"https:\/\/mb668s.com\/cam-nang-7mb66-xoc-dia\/tu-van-nghe-nghiep\/convolutional-neural-network-la-gi","title":{"rendered":"Convolutional Neural Network l\u00e0 g\u00ec, c\u00f3 c\u1ea5u tr\u00fac th\u1ebf n\u00e0o?"},"content":{"rendered":"

Convolutional Neural Network l\u00e0 m\u1ed9t trong nh\u1eefng thu\u1eadt ng\u1eef quen thu\u1ed9c trong l\u0129nh v\u1ef1c c\u00f4ng ngh\u1ec7 th\u00f4ng tin. V\u00e0 thu\u1eadt to\u00e1n n\u00e0y \u0111\u00f3ng vai tr\u00f2 r\u1ea5t quan tr\u1ecdng v\u1edbi c\u00e1c l\u1eadp tr\u00ecnh vi\u00ean ho\u1eb7c nh\u1eefng ng\u01b0\u1eddi l\u00e0m v\u1ec1 IT. Trong b\u00e0i vi\u1ebft n\u00e0y, CareerLink<\/strong> s\u1ebd gi\u1ea3i \u0111\u00e1p chi ti\u1ebft Convolutional Neural Network l\u00e0 g\u00ec<\/strong>, c\u1ea5u tr\u00fac v\u00e0 \u00fd t\u01b0\u1edfng x\u00e2y d\u1ef1ng \u0111\u1ec3 c\u00e1c b\u1ea1n c\u00f3 th\u1ec3 hi\u1ec3u r\u00f5 h\u01a1n.<\/p>\n

\"Convolutional<\/figure>\n

Convolutional Neural Network l\u00e0 g\u00ec?<\/h2>\n

Convolutional Neural Network \u0111\u01b0\u1ee3c c\u00e1c chuy\u00ean gia trong l\u0129nh v\u1ef1c CNTT \u0111\u00e1nh gi\u00e1 l\u00e0 m\u1ed9t thu\u1eadt to\u00e1n c\u00f3 \u0111\u1ed9 ch\u00ednh x\u00e1c v\u00f4 c\u00f9ng cao v\u00e0 \u0111\u01b0\u1ee3c \u1ee9ng d\u1ee5ng r\u1ed9ng r\u00e3i hi\u1ec7n nay.<\/p>\n

\n

\u201cConvolutional Neural Network\u00a0(CNN) l\u00e0 \u201cm\u1ea1ng N\u01a1-ron t\u00edch ch\u1eadp – m\u1ed9t d\u1ea1ng ki\u1ebfn tr\u00fac m\u1ea1ng n\u01a1-ron s\u00e2u (Deep Neural Network) \u0111\u01b0\u1ee3c s\u1eed d\u1ee5ng trong l\u0129nh v\u1ef1c tr\u00ed tu\u1ec7 nh\u00e2n t\u1ea1o, th\u1ecb gi\u00e1c m\u00e1y t\u00ednh, \u0111\u1eb7c bi\u1ec7t l\u00e0 trong vi\u1ec7c x\u1eed l\u00fd v\u00e0 nh\u1eadn di\u1ec7n h\u00ecnh \u1ea3nh.\u201d<\/strong><\/p>\n<\/blockquote>\n

C\u1ea5u tr\u00fac CNN \u0111\u01b0\u1ee3c l\u1ea5y c\u1ea3m h\u1ee9ng t\u1eeb c\u00e1c m\u00f4 h\u00ecnh k\u1ebft n\u1ed1i c\u1ee7a n\u00e3o ng\u01b0\u1eddi, \u0111\u1eb7c bi\u1ec7t l\u00e0 v\u1ecf n\u00e3o th\u1ecb gi\u00e1c, \u0111\u00f3ng vai tr\u00f2 thi\u1ebft y\u1ebfu trong vi\u1ec7c nh\u1eadn bi\u1ebft v\u00e0 x\u1eed l\u00fd c\u00e1c k\u00edch th\u00edch th\u1ecb gi\u00e1c. C\u00e1c n\u01a1-ron nh\u00e2n t\u1ea1o trong CNN \u0111\u01b0\u1ee3c s\u1eafp x\u1ebfp \u0111\u1ec3 di\u1ec5n gi\u1ea3i th\u00f4ng tin h\u00ecnh \u1ea3nh m\u1ed9t c\u00e1ch hi\u1ec7u qu\u1ea3, cho ph\u00e9p c\u00e1c m\u00f4 h\u00ecnh n\u00e0y x\u1eed l\u00fd to\u00e0n b\u1ed9 h\u00ecnh \u1ea3nh. V\u00ec CNN r\u1ea5t hi\u1ec7u qu\u1ea3 trong vi\u1ec7c x\u00e1c \u0111\u1ecbnh \u0111\u1ed1i t\u01b0\u1ee3ng n\u00ean ch\u00fang th\u01b0\u1eddng \u0111\u01b0\u1ee3c s\u1eed d\u1ee5ng cho c\u00e1c t\u00e1c v\u1ee5 th\u1ecb gi\u00e1c m\u00e1y t\u00ednh nh\u01b0 nh\u1eadn d\u1ea1ng h\u00ecnh \u1ea3nh v\u00e0 ph\u00e1t hi\u1ec7n \u0111\u1ed1i t\u01b0\u1ee3ng, v\u1edbi c\u00e1c tr\u01b0\u1eddng h\u1ee3p s\u1eed d\u1ee5ng ph\u1ed5 bi\u1ebfn bao g\u1ed3m \u00f4 t\u00f4 t\u1ef1 l\u00e1i, nh\u1eadn d\u1ea1ng khu\u00f4n m\u1eb7t v\u00e0 ph\u00e2n t\u00edch h\u00ecnh \u1ea3nh y t\u1ebf.<\/p>\n

CNN s\u1eed d\u1ee5ng m\u1ed9t lo\u1ea1t c\u00e1c l\u1edbp, m\u1ed7i l\u1edbp ph\u00e1t hi\u1ec7n c\u00e1c \u0111\u1eb7c \u0111i\u1ec3m kh\u00e1c nhau c\u1ee7a h\u00ecnh \u1ea3nh \u0111\u1ea7u v\u00e0o. T\u00f9y v\u00e0o m\u1ee5c \u0111\u00edch, m\u1ed9t CNN c\u00f3 th\u1ec3 ch\u1ee9a h\u00e0ng ch\u1ee5c, h\u00e0ng tr\u0103m ho\u1eb7c th\u1eadm ch\u00ed h\u00e0ng ngh\u00ecn l\u1edbp \u0111\u1ec3 nh\u1eadn d\u1ea1ng c\u00e1c m\u1eabu chi ti\u1ebft. C\u00e1ch ti\u1ebfp c\u1eadn to\u00e0n di\u1ec7n n\u00e0y c\u1ee7a CNN gi\u00fap n\u00f3 v\u01b0\u1ee3t tr\u1ed9i h\u01a1n m\u1ea1ng n\u01a1 ron truy\u1ec1n th\u1ed1ng trong m\u1ed9t lo\u1ea1t c\u00e1c nhi\u1ec7m v\u1ee5 li\u00ean quan \u0111\u1ebfn h\u00ecnh \u1ea3nh.<\/p>\n

Convolutional Neural Network c\u00f3 c\u1ea5u tr\u00fac nh\u01b0 th\u1ebf n\u00e0o?<\/h2>\n

C\u1ea5u tr\u00fac m\u1ea1ng Convolutional Neural Network th\u01b0\u1eddng \u0111\u01b0\u1ee3c chia th\u00e0nh m\u1ed9t s\u1ed1 l\u1edbp c\u01a1 b\u1ea3n, \u0111\u00f3 l\u00e0:<\/p>\n

L\u1edbp convolutional layer (l\u1edbp ch\u1eadp)<\/h3>\n

C\u00f3 th\u1ec3 n\u00f3i convolutional layer (l\u1edbp ch\u1eadp) l\u00e0 m\u1ed9t l\u1edbp c\u1ef1c k\u1ef3 quan tr\u1ecdng trong CNN v\u00ec n\u00f3 cho ph\u00e9p b\u1ea1n th\u1ef1c hi\u1ec7n \u0111\u01b0\u1ee3c m\u1ecdi ph\u00e9p t\u00ednh. M\u1ed9t s\u1ed1 kh\u00e1i ni\u1ec7m c\u1ea7n bi\u1ebft \u1edf convolutional layer nh\u01b0: <\/p>\n

– Filter map: N\u1ebfu nh\u01b0 ANN k\u1ebft n\u1ed1i v\u1edbi t\u1eebng pixel c\u1ee7a h\u00ecnh \u1ea3nh \u0111\u1ea7u v\u00e0o th\u00ec CNN s\u1eed d\u1ee5ng nh\u1eefng filter \u0111\u1ec3 \u00e1p v\u00e0o nh\u1eefng v\u00f9ng tr\u00ean h\u00ecnh \u1ea3nh. C\u00e1c filter map n\u00e0y l\u00e0 m\u1ed9t ma tr\u1eadn 3 chi\u1ec1u, trong \u0111\u00f3 g\u1ed3m nh\u1eefng con s\u1ed1. V\u00e0 c\u00e1c con s\u1ed1 \u0111\u00f3 l\u1ea1i ch\u00ednh l\u00e0 parameter.<\/p>\n

– Padding: nh\u1eefng gi\u00e1 tr\u1ecb b\u1eb1ng 0 \u0111\u01b0\u1ee3c th\u00eam v\u00e0o l\u1edbp input.<\/p>\n

– Feature map: th\u1ec3 hi\u1ec7n k\u1ebft qu\u1ea3 m\u1ed7i l\u1ea7n filter map d\u1ecbch chuy\u1ec3n qu\u00e9t qua input. M\u1ed7i l\u1ea7n qu\u00e9t nh\u01b0 v\u1eady s\u1ebd x\u1ea3y ra qu\u00e1 tr\u00ecnh t\u00ednh to\u00e1n.<\/p>\n

– Stride: bi\u1ec3u th\u1ecb s\u1ef1 d\u1ecbch chuy\u1ec3n c\u1ee7a filter map qua h\u00ecnh \u1ea3nh theo gi\u00e1 tr\u1ecb t\u1eeb tr\u00e1i qua ph\u1ea3i.<\/p>\n

Activation Function (ch\u1ee9c n\u0103ng k\u00edch ho\u1ea1t)<\/h3>\n

Sau khi t\u00ednh to\u00e1n convolution, m\u1ed9t activation function nh\u01b0 h\u00e0m ReLU (Rectified Linear Unit) \u0111\u01b0\u1ee3c d\u00f9ng \u0111\u1ec3 t\u00ednh phi tuy\u1ebfn cho m\u1ea1ng CNN v\u1edbi c\u00e1c gi\u00e1 tr\u1ecb tuy\u1ebfn t\u00ednh kh\u00f4ng \u00e2m.<\/p>\n

Ngo\u00e0i ra, c\u00f2n c\u00f3 m\u1ed9t s\u1ed1 h\u00e0m phi tuy\u1ebfn kh\u00e1c nh\u01b0 sigmoid, tanh c\u0169ng c\u00f3 th\u1ec3 thay th\u1ebf cho h\u00e0m ReLU nh\u01b0ng ReLU th\u01b0\u1eddng \u0111\u01b0\u1ee3c l\u1ef1a ch\u1ecdn s\u1eed d\u1ee5ng b\u1edfi hi\u1ec7u su\u1ea5t t\u1ed1t.<\/p>\n

Pooling layer (l\u1edbp g\u1ed9p)<\/h3>\n

L\u1edbp g\u1ed9p c\u00f3 t\u00e1c d\u1ee5ng l\u00e0m gi\u1ea3m k\u00edch th\u01b0\u1edbc c\u1ee7a Feature map t\u1eeb l\u1edbp convolutional layer b\u1eb1ng c\u00e1ch l\u1ea5y gi\u00e1 tr\u1ecb l\u1edbn nh\u1ea5t (max pooling) ho\u1eb7c gi\u00e1 tr\u1ecb trung b\u00ecnh (average pooling) t\u1eeb m\u1ed9t v\u00f9ng c\u1ee5 th\u1ec3 tr\u00ean Feature map. <\/p>\n

Fully connected layer (l\u1edbp \u0111\u01b0\u1ee3c k\u1ebft n\u1ed1i \u0111\u1ea7y \u0111\u1ee7) <\/h3>\n

Fully Connected Layer c\u00f3 vai tr\u00f2 \u0111\u01b0a ra k\u1ebft qu\u1ea3 sau khi \u0111\u00e3 qua l\u1edbp ch\u1eadp v\u00e0 l\u1edbp g\u1ed9p. T\u1ea1i \u0111\u00e2y, s\u1ebd thu \u0111\u01b0\u1ee3c k\u1ebft qu\u1ea3 m\u00f4 h\u00ecnh \u0111\u00e3 l\u1ecdc th\u00f4ng tin c\u1ee7a \u1ea3nh v\u00e0 c\u00f3 th\u1ec3 li\u00ean k\u1ebft v\u1edbi nhau \u0111\u1ec3 \u0111\u01b0a ra c\u00e1c d\u1ef1 \u0111o\u00e1n ph\u1ee9c t\u1ea1p.<\/p>\n

N\u1ebfu fully connected layer nh\u1eadn d\u1eef li\u1ec7u h\u00ecnh \u1ea3nh, n\u00f3 s\u1ebd chuy\u1ec3n th\u00e0nh d\u1ea1ng ch\u01b0a \u0111\u01b0\u1ee3c ph\u00e2n lo\u1ea1i, gi\u1ed1ng nh\u01b0 vi\u1ec7c th\u1ef1c hi\u1ec7n phi\u1ebfu b\u1ea7u v\u00e0 \u0111\u00e1nh gi\u00e1 \u0111\u1ec3 l\u1ef1a ch\u1ecdn ra h\u00ecnh \u1ea3nh ch\u1ea5t l\u01b0\u1ee3ng cao nh\u1ea5t.<\/p>\n

Output layer (l\u1edbp cu\u1ed1i c\u00f9ng)<\/h3>\n

L\u1edbp cu\u1ed1i c\u00f9ng (output layer) c\u00f3 ch\u1ee9a c\u00e1c n\u01a1 ron l\u00e0 \u0111\u1ea1i di\u1ec7n cho c\u00e1c l\u1edbp ph\u00e2n lo\u1ea1i v\u00e0 s\u1eed d\u1ee5ng h\u00e0m softmax \u0111\u1ec3 t\u00ednh to\u00e1n x\u00e1c su\u1ea5t ph\u00e2n lo\u1ea1i.<\/p>\n

Convolutional Neural Network \u0111\u01b0\u1ee3c x\u00e2y d\u1ef1ng d\u1ef1a tr\u00ean \u00fd t\u01b0\u1edfng n\u00e0o?<\/h2>\n

\u0110\u1ebfn \u0111\u00e2y, b\u1ea1n \u0111\u1ecdc \u0111\u00e3 n\u1eafm \u0111\u01b0\u1ee3c kh\u00e1i ni\u1ec7m v\u1ec1 Convolutional Neural Network l\u00e0 g\u00ec. Ti\u1ebfp theo h\u00e3y c\u00f9ng t\u00ecm hi\u1ec3u ti\u1ebfp v\u1ec1 \u00fd t\u01b0\u1edfng x\u00e2y d\u1ef1ng m\u1ea1ng CNN l\u00e0 nh\u1eefng g\u00ec nh\u00e9.<\/p>\n

CNN l\u00e0 m\u1ed9t trong nh\u1eefng t\u1eadp h\u1ee3p c\u1ee7a l\u1edbp Convolution ch\u1ed3ng l\u00ean nhau. S\u1eed d\u1ee5ng c\u00e1c h\u00e0m nonlinear activation ReLU v\u00e0 tanh \u0111\u1ec3 k\u00edch ho\u1ea1t tr\u1ecdng s\u1ed1. Khi \u0111\u00e3 qua h\u00e0m th\u00ec l\u1edbp n\u00e0y s\u1ebd thu \u0111\u01b0\u1ee3c tr\u1ecdng s\u1ed1 trong c\u00e1c node, cho ra nh\u1eefng th\u00f4ng tin tr\u1eebu t\u01b0\u1ee3ng h\u01a1n cho c\u00e1c l\u1edbp ti\u1ebfp theo.<\/p>\n

\u0110\u1eb7c bi\u1ec7t CNN c\u00f3 2 kh\u00eda c\u1ea1nh c\u1ea7n ph\u1ea3i quan t\u00e2m \u0111\u00f3 l\u00e0 t\u00ednh b\u1ea5t bi\u1ebfn (Location Invariance) v\u00e0 t\u00ednh k\u1ebft h\u1ee3p (compositionality). Trong tr\u01b0\u1eddng h\u1ee3p, n\u1ebfu c\u00f9ng m\u1ed9t \u0111\u1ed1i t\u01b0\u1ee3ng nh\u01b0ng chi\u1ebfu theo nh\u1eefng g\u00f3c kh\u00e1c nhau (translation, rotation, scaling) th\u00ec s\u1ebd cho \u0111\u1ed9 ch\u00ednh x\u00e1c s\u1ebd b\u1ecb \u1ea3nh h\u01b0\u1edfng \u0111\u00e1ng k\u1ec3.<\/p>\n

Pooling layer s\u1ebd l\u00e0m b\u1ea5t bi\u1ebfn t\u00ednh ch\u1ea5t \u0111\u1ed1i v\u1edbi translation (ph\u00e9p d\u1ecbch chuy\u1ec3n), rotation (ph\u00e9p quay) v\u00e0 scaling (ph\u00e9p co gi\u00e3n). T\u00ednh k\u1ebft h\u1ee3p c\u1ee5c b\u1ed9 t\u1ea1o ra c\u00e1c c\u1ea5p \u0111\u1ed9 bi\u1ec3u di\u1ec5n th\u00f4ng tin t\u1eeb th\u1ea5p \u0111\u1ebfn cao v\u00e0 tr\u1eebu t\u01b0\u1ee3ng h\u01a1n qua convolution t\u1eeb c\u00e1c filter.<\/p>\n

M\u1ea1ng CNN s\u1eed d\u1ee5ng ba \u00fd t\u01b0\u1edfng c\u01a1 b\u1ea3n sau \u0111\u00e2y \u0111\u1ec3 x\u1eed l\u00fd d\u1eef li\u1ec7u h\u00ecnh \u1ea3nh hi\u1ec7u qu\u1ea3:<\/p>\n

Local Receptive Field (l\u1edbp tr\u01b0\u1eddng c\u1ee5c b\u1ed9)<\/h3>\n

Nhi\u1ec7m v\u1ee5 c\u1ee7a l\u1edbp tr\u01b0\u1eddng c\u1ee5c b\u1ed9 n\u00e0y l\u00e0 chia, t\u00e1ch l\u1ecdc c\u00e1c d\u1eef li\u1ec7u, th\u00f4ng tin c\u1ee7a \u1ea3nh th\u00e0nh c\u00e1c ph\u1ea7n nh\u1ecf \u0111\u1ec3 x\u00e1c \u0111\u1ecbnh v\u00f9ng quan tr\u1ecdng, t\u1eeb \u0111\u00f3 ch\u1ecdn ra nh\u1eefng v\u00f9ng \u1ea3nh c\u00f3 gi\u00e1 tr\u1ecb cao nh\u1ea5t.<\/p>\n

Shared Weights and Bias (l\u1edbp tr\u1ecdng s\u1ed1 chia s\u1ebb)<\/h3>\n

L\u1edbp n\u00e0y h\u1ed7 tr\u1ee3 l\u00e0m gi\u1ea3m t\u1ed1i \u0111a c\u00e1c tham s\u1ed1 xu\u1ed1ng m\u1ee9c t\u1ed1i thi\u1ec3u trong CNN. B\u1edfi trong m\u1ed7i convolution ch\u1ee9a ri\u00eang t\u1eebng feature map v\u00e0 t\u1eebng feature l\u1ea1i gi\u00fap detect m\u1ed9t v\u00e0i feature trong h\u00ecnh \u1ea3nh.<\/p>\n

Pooling layer (l\u1edbp t\u1ed5ng h\u1ee3p) <\/h3>\n

\u0110\u00e2y g\u1ea7n nh\u01b0 l\u00e0 l\u1edbp cu\u1ed1i c\u00f9ng tr\u01b0\u1edbc khi cho ra k\u1ebft qu\u1ea3 nh\u01b0 k\u1ef3 mong mu\u1ed1n. V\u00e0 \u0111\u1ec3 c\u00f3 \u0111\u01b0\u1ee3c k\u1ebft qu\u1ea3 d\u1ec5 hi\u1ec3u v\u00e0 d\u1ec5 d\u00f9ng nh\u1ea5t th\u00ec pooling layer s\u1ebd l\u00e0m \u0111\u01a1n gi\u1ea3n h\u00f3a th\u00f4ng tin \u0111\u1ea7u ra (output). Ngh\u0129a l\u00e0, sau khi t\u00ednh to\u00e1n xong v\u00e0 qu\u00e9t qua c\u00e1c l\u1edbp layer th\u00ec s\u1ebd \u0111i \u0111\u1ebfn pooling layer \u0111\u1ec3 l\u01b0\u1ee3c b\u1edbt nh\u1eefng th\u00f4ng tin kh\u00f4ng c\u1ea7n thi\u1ebft.<\/p>\n

H\u01b0\u1edbng d\u1eabn c\u00e1ch ch\u1ecdn tham s\u1ed1 ph\u00f9 h\u1ee3p cho CNN<\/h2>\n

\u0110\u1ec3 ch\u1ecdn \u0111\u01b0\u1ee3c tham s\u1ed1 ph\u00f9 h\u1ee3p nh\u1ea5t v\u1edbi Convolutional Neural Network, c\u00e1c b\u1ea1n n\u00ean l\u01b0u \u00fd \u0111\u1ebfn s\u1ed1 l\u01b0\u1ee3ng c\u00e1c y\u1ebfu t\u1ed1: filter size, pooling size, convolution v\u00e0 s\u1ed1 l\u01b0\u1ee3t train test. C\u1ee5 th\u1ec3 nh\u01b0:<\/p>\n

– Convolution layer: S\u1eed d\u1ee5ng l\u1edbp ch\u1eadp v\u1edbi s\u1ed1 l\u01b0\u1ee3ng nhi\u1ec1u s\u1ebd gi\u00fap c\u00e1c t\u00e1c \u0111\u1ed9ng gi\u1ea3m \u0111i m\u1ed9t c\u00e1ch \u0111\u00e1ng k\u1ec3. Trong ph\u1ea7n l\u1edbn c\u00e1c tr\u01b0\u1eddng h\u1ee3p, ch\u1ec9 c\u1ea7n kho\u1ea3ng 3 \u0111\u1ebfn 5 l\u1edbp c\u0169ng \u0111\u00e3 thu v\u1ec1 k\u1ebft qu\u1ea3 nh\u01b0 mong \u0111\u1ee3i. C\u00f2n n\u1ebfu s\u1ed1 l\u01b0\u1ee3ng l\u1edbp n\u00e0y l\u1edbn h\u01a1n th\u00ec hi\u1ec7u su\u1ea5t cao h\u01a1n nh\u01b0ng h\u00e3y c\u00e2n nh\u1eafc \u0111\u1ec3 tr\u00e1nh l\u00e0m l\u00e3ng ph\u00ed t\u00e0i nguy\u00ean.<\/p>\n

– Filter size:  th\u01b0\u1eddng th\u00ec c\u00e1c filter size s\u1ebd c\u00f3 k\u00edch th\u01b0\u1edbc ph\u1ed5 bi\u1ebfn l\u00e0 3\u00d73 ho\u1eb7c 5\u00d75. V\u00e0 k\u00edch th\u01b0\u1edbc n\u00e0y c\u00f3 th\u1ec3 \u0111i\u1ec1u ch\u1ec9nh \u0111\u1ec3 ph\u00f9 h\u1ee3p v\u1edbi t\u1eebng d\u1eef li\u1ec7u c\u1ee5 th\u1ec3.<\/p>\n

– Pooling size: V\u1edbi c\u00e1c h\u00ecnh \u1ea3nh th\u00f4ng th\u01b0\u1eddng s\u1ebd s\u1eed d\u1ee5ng lo\u1ea1i k\u00edch th\u01b0\u1edbc 2\u00d72 cho ra h\u00ecnh \u1ea3nh ti\u00eau chu\u1ea9n. N\u1ebfu \u0111\u1ea7u v\u00e0o c\u00f3 d\u1ea1ng h\u00ecnh \u1ea3nh l\u1edbn h\u01a1n th\u00ec n\u00ean chuy\u1ec3n sang k\u00edch th\u01b0\u1edbc 4\u00d74. <\/p>\n

– Train test: C\u00e0ng th\u1ef1c hi\u1ec7n train test \u0111\u1ec3 t\u1ed1i \u01b0u h\u00f3a tham s\u1ed1 nhi\u1ec1u l\u1ea7n th\u00ec c\u00e0ng c\u00f3 nhi\u1ec1u kh\u1ea3 n\u0103ng thu \u0111\u01b0\u1ee3c c\u00e1c parameter t\u1ed1t. \u0110i\u1ec1u n\u00e0y gi\u00fap cho m\u00f4 h\u00ecnh tr\u1edf n\u00ean \u201cth\u00f4ng minh\u201d v\u00e0 hi\u1ec7u qu\u1ea3 h\u01a1n.<\/p>\n

Hy v\u1ecdng, v\u1edbi th\u00f4ng tin b\u00e0i vi\u1ebft \u0111\u01b0\u1ee3c chia s\u1ebb \u1edf tr\u00ean, b\u1ea1n \u0111\u1ecdc s\u1ebd hi\u1ec3u h\u01a1n\u00a0v\u1ec1\u00a0Convolutional Neural Network l\u00e0 g\u00ec<\/strong>, c\u1ea5u tr\u00fac v\u00e0 c\u00e1ch l\u1ef1a ch\u1ecdn tham s\u1ed1 cho m\u1ea1ng CNN nh\u01b0 th\u1ebf n\u00e0o. Truy c\u1eadp Careerlink.vn<\/strong> \u0111\u1ec3 t\u00ecm hi\u1ec3u th\u00eam v\u1ec1 c\u00e1c thu\u1eadt ng\u1eef kh\u00e1c v\u00e0 c\u00e1c th\u00f4ng tin vi\u1ec7c l\u00e0m h\u1ea5p d\u1eabn nh\u00e9.<\/p>\n

Th\u00fay Vui<\/strong><\/p>\n","protected":false},"excerpt":{"rendered":"

Convolutional Neural Network l\u00e0 m\u1ed9t trong nh\u1eefng thu\u1eadt ng\u1eef quen thu\u1ed9c trong l\u0129nh v\u1ef1c c\u00f4ng ngh\u1ec7 th\u00f4ng tin. V\u00e0 thu\u1eadt to\u00e1n n\u00e0y \u0111\u00f3ng vai tr\u00f2 …<\/p>\n","protected":false},"author":58,"featured_media":8616,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[17],"tags":[],"class_list":["post-8613","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-tu-van-nghe-nghiep"],"_links":{"self":[{"href":"https:\/\/mb668s.com\/cam-nang-7mb66-xoc-dia\/wp-json\/wp\/v2\/posts\/8613","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/mb668s.com\/cam-nang-7mb66-xoc-dia\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/mb668s.com\/cam-nang-7mb66-xoc-dia\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/mb668s.com\/cam-nang-7mb66-xoc-dia\/wp-json\/wp\/v2\/users\/58"}],"replies":[{"embeddable":true,"href":"https:\/\/mb668s.com\/cam-nang-7mb66-xoc-dia\/wp-json\/wp\/v2\/comments?post=8613"}],"version-history":[{"count":2,"href":"https:\/\/mb668s.com\/cam-nang-7mb66-xoc-dia\/wp-json\/wp\/v2\/posts\/8613\/revisions"}],"predecessor-version":[{"id":15191,"href":"https:\/\/mb668s.com\/cam-nang-7mb66-xoc-dia\/wp-json\/wp\/v2\/posts\/8613\/revisions\/15191"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/mb668s.com\/cam-nang-7mb66-xoc-dia\/wp-json\/wp\/v2\/media\/8616"}],"wp:attachment":[{"href":"https:\/\/mb668s.com\/cam-nang-7mb66-xoc-dia\/wp-json\/wp\/v2\/media?parent=8613"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/mb668s.com\/cam-nang-7mb66-xoc-dia\/wp-json\/wp\/v2\/categories?post=8613"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/mb668s.com\/cam-nang-7mb66-xoc-dia\/wp-json\/wp\/v2\/tags?post=8613"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}