{"id":479160,"date":"2023-08-09T10:31:59","date_gmt":"2023-08-09T10:31:59","guid":{"rendered":""},"modified":"2023-09-05T11:18:19","modified_gmt":"2023-09-05T11:18:19","slug":"stochastic-gradient-descent","status":"publish","type":"wiki","link":"https:\/\/oneproxy.pro\/vn\/wiki\/stochastic-gradient-descent\/","title":{"rendered":"Gi\u1ea3m d\u1ea7n \u0111\u1ed9 d\u1ed1c ng\u1eabu nhi\u00ean"},"content":{"rendered":"<p>Stochastic gradient Descent (SGD) l\u00e0 m\u1ed9t thu\u1eadt to\u00e1n t\u1ed1i \u01b0u h\u00f3a ph\u1ed5 bi\u1ebfn \u0111\u01b0\u1ee3c s\u1eed d\u1ee5ng r\u1ed9ng r\u00e3i trong h\u1ecdc m\u00e1y v\u00e0 h\u1ecdc s\u00e2u. N\u00f3 \u0111\u00f3ng m\u1ed9t vai tr\u00f2 quan tr\u1ecdng trong vi\u1ec7c \u0111\u00e0o t\u1ea1o c\u00e1c m\u00f4 h\u00ecnh cho c\u00e1c \u1ee9ng d\u1ee5ng kh\u00e1c nhau, bao g\u1ed3m nh\u1eadn d\u1ea1ng h\u00ecnh \u1ea3nh, x\u1eed l\u00fd ng\u00f4n ng\u1eef t\u1ef1 nhi\u00ean v\u00e0 h\u1ec7 th\u1ed1ng \u0111\u1ec1 xu\u1ea5t. SGD l\u00e0 ph\u1ea7n m\u1edf r\u1ed9ng c\u1ee7a thu\u1eadt to\u00e1n gi\u1ea3m \u0111\u1ed9 d\u1ed1c v\u00e0 nh\u1eb1m m\u1ee5c \u0111\u00edch t\u00ecm ra c\u00e1c tham s\u1ed1 t\u1ed1i \u01b0u c\u1ee7a m\u00f4 h\u00ecnh m\u1ed9t c\u00e1ch hi\u1ec7u qu\u1ea3 b\u1eb1ng c\u00e1ch c\u1eadp nh\u1eadt l\u1eb7p l\u1ea1i ch\u00fang d\u1ef1a tr\u00ean c\u00e1c t\u1eadp h\u1ee3p con nh\u1ecf c\u1ee7a d\u1eef li\u1ec7u hu\u1ea5n luy\u1ec7n, \u0111\u01b0\u1ee3c g\u1ecdi l\u00e0 c\u00e1c l\u00f4 nh\u1ecf.<\/p>\n<h2>L\u1ecbch s\u1eed v\u1ec1 ngu\u1ed3n g\u1ed1c c\u1ee7a Stochastic gradient Descent v\u00e0 l\u1ea7n \u0111\u1ea7u ti\u00ean \u0111\u1ec1 c\u1eadp \u0111\u1ebfn n\u00f3<\/h2>\n<p>Kh\u00e1i ni\u1ec7m t\u1ed1i \u01b0u h\u00f3a ng\u1eabu nhi\u00ean c\u00f3 t\u1eeb \u0111\u1ea7u nh\u1eefng n\u0103m 1950 khi c\u00e1c nh\u00e0 nghi\u00ean c\u1ee9u kh\u00e1m ph\u00e1 c\u00e1c k\u1ef9 thu\u1eadt t\u1ed1i \u01b0u h\u00f3a kh\u00e1c nhau. Tuy nhi\u00ean, l\u1ea7n \u0111\u1ea7u ti\u00ean \u0111\u1ec1 c\u1eadp \u0111\u1ebfn Gi\u1ea3m d\u1ea7n \u0111\u1ed9 d\u1ed1c ng\u1eabu nhi\u00ean trong b\u1ed1i c\u1ea3nh h\u1ecdc m\u00e1y c\u00f3 th\u1ec3 b\u1eaft ngu\u1ed3n t\u1eeb nh\u1eefng n\u0103m 1960. \u00dd t\u01b0\u1edfng n\u00e0y tr\u1edf n\u00ean ph\u1ed5 bi\u1ebfn v\u00e0o nh\u1eefng n\u0103m 1980 v\u00e0 1990 khi n\u00f3 \u0111\u01b0\u1ee3c ch\u1ee9ng minh l\u00e0 c\u00f3 hi\u1ec7u qu\u1ea3 trong vi\u1ec7c \u0111\u00e0o t\u1ea1o m\u1ea1ng l\u01b0\u1edbi th\u1ea7n kinh v\u00e0 c\u00e1c m\u00f4 h\u00ecnh ph\u1ee9c t\u1ea1p kh\u00e1c.<\/p>\n<h2>Th\u00f4ng tin chi ti\u1ebft v\u1ec1 Gi\u1ea3m d\u1ea7n \u0111\u1ed9 d\u1ed1c ng\u1eabu nhi\u00ean<\/h2>\n<p>SGD l\u00e0 m\u1ed9t thu\u1eadt to\u00e1n t\u1ed1i \u01b0u h\u00f3a l\u1eb7p l\u1ea1i nh\u1eb1m m\u1ee5c \u0111\u00edch gi\u1ea3m thi\u1ec3u h\u00e0m m\u1ea5t m\u00e1t b\u1eb1ng c\u00e1ch \u0111i\u1ec1u ch\u1ec9nh c\u00e1c tham s\u1ed1 c\u1ee7a m\u00f4 h\u00ecnh. Kh\u00f4ng gi\u1ed1ng nh\u01b0 gi\u1ea3m \u0111\u1ed9 d\u1ed1c truy\u1ec1n th\u1ed1ng, t\u00ednh to\u00e1n \u0111\u1ed9 d\u1ed1c b\u1eb1ng c\u00e1ch s\u1eed d\u1ee5ng to\u00e0n b\u1ed9 t\u1eadp d\u1eef li\u1ec7u hu\u1ea5n luy\u1ec7n (gi\u1ea3m \u0111\u1ed9 d\u1ed1c h\u00e0ng lo\u1ea1t), SGD l\u1ea5y m\u1eabu ng\u1eabu nhi\u00ean m\u1ed9t lo\u1ea1t \u0111i\u1ec3m d\u1eef li\u1ec7u nh\u1ecf v\u00e0 c\u1eadp nh\u1eadt c\u00e1c tham s\u1ed1 d\u1ef1a tr\u00ean \u0111\u1ed9 d\u1ed1c c\u1ee7a h\u00e0m m\u1ea5t m\u00e1t \u0111\u01b0\u1ee3c t\u00ednh to\u00e1n tr\u00ean l\u00f4 nh\u1ecf n\u00e0y.<\/p>\n<p>C\u00e1c b\u01b0\u1edbc ch\u00ednh li\u00ean quan \u0111\u1ebfn thu\u1eadt to\u00e1n Gi\u1ea3m d\u1ea7n \u0111\u1ed9 d\u1ed1c ng\u1eabu nhi\u00ean nh\u01b0 sau:<\/p>\n<ol>\n<li>Kh\u1edfi t\u1ea1o c\u00e1c tham s\u1ed1 m\u00f4 h\u00ecnh m\u1ed9t c\u00e1ch ng\u1eabu nhi\u00ean.<\/li>\n<li>X\u00e1o tr\u1ed9n ng\u1eabu nhi\u00ean t\u1eadp d\u1eef li\u1ec7u hu\u1ea5n luy\u1ec7n.<\/li>\n<li>Chia d\u1eef li\u1ec7u th\u00e0nh c\u00e1c l\u00f4 nh\u1ecf.<\/li>\n<li>\u0110\u1ed1i v\u1edbi m\u1ed7i l\u00f4 nh\u1ecf, h\u00e3y t\u00ednh \u0111\u1ed9 d\u1ed1c c\u1ee7a h\u00e0m m\u1ea5t m\u00e1t \u0111\u1ed1i v\u1edbi c\u00e1c tham s\u1ed1.<\/li>\n<li>C\u1eadp nh\u1eadt c\u00e1c tham s\u1ed1 m\u00f4 h\u00ecnh b\u1eb1ng c\u00e1ch s\u1eed d\u1ee5ng gradient \u0111\u01b0\u1ee3c t\u00ednh to\u00e1n v\u00e0 t\u1ed1c \u0111\u1ed9 h\u1ecdc t\u1eadp, ki\u1ec3m so\u00e1t k\u00edch th\u01b0\u1edbc b\u01b0\u1edbc c\u1ee7a c\u00e1c b\u1ea3n c\u1eadp nh\u1eadt.<\/li>\n<li>L\u1eb7p l\u1ea1i quy tr\u00ecnh v\u1edbi s\u1ed1 l\u1ea7n l\u1eb7p c\u1ed1 \u0111\u1ecbnh ho\u1eb7c cho \u0111\u1ebfn khi \u0111\u00e1p \u1ee9ng ti\u00eau ch\u00ed h\u1ed9i t\u1ee5.<\/li>\n<\/ol>\n<h2>C\u1ea5u tr\u00fac b\u00ean trong c\u1ee7a Stochastic gradient Descent \u2013 SGD ho\u1ea1t \u0111\u1ed9ng nh\u01b0 th\u1ebf n\u00e0o<\/h2>\n<p>\u00dd t\u01b0\u1edfng ch\u00ednh \u0111\u1eb1ng sau Stochastic gradient Descent l\u00e0 gi\u1edbi thi\u1ec7u t\u00ednh ng\u1eabu nhi\u00ean trong c\u00e1c c\u1eadp nh\u1eadt tham s\u1ed1 b\u1eb1ng c\u00e1ch s\u1eed d\u1ee5ng c\u00e1c \u0111\u1ee3t nh\u1ecf. T\u00ednh ng\u1eabu nhi\u00ean n\u00e0y th\u01b0\u1eddng d\u1eabn \u0111\u1ebfn s\u1ef1 h\u1ed9i t\u1ee5 nhanh h\u01a1n v\u00e0 c\u00f3 th\u1ec3 gi\u00fap tho\u00e1t kh\u1ecfi c\u1ef1c ti\u1ec3u c\u1ee5c b\u1ed9 trong qu\u00e1 tr\u00ecnh t\u1ed1i \u01b0u h\u00f3a. Tuy nhi\u00ean, t\u00ednh ng\u1eabu nhi\u00ean c\u0169ng c\u00f3 th\u1ec3 khi\u1ebfn qu\u00e1 tr\u00ecnh t\u1ed1i \u01b0u dao \u0111\u1ed9ng xung quanh l\u1eddi gi\u1ea3i t\u1ed1i \u01b0u.<\/p>\n<p>SGD c\u00f3 hi\u1ec7u qu\u1ea3 v\u1ec1 m\u1eb7t t\u00ednh to\u00e1n, \u0111\u1eb7c bi\u1ec7t \u0111\u1ed1i v\u1edbi c\u00e1c t\u1eadp d\u1eef li\u1ec7u l\u1edbn, v\u00ec n\u00f3 ch\u1ec9 x\u1eed l\u00fd m\u1ed9t t\u1eadp h\u1ee3p con d\u1eef li\u1ec7u nh\u1ecf trong m\u1ed7i l\u1ea7n l\u1eb7p. Thu\u1ed9c t\u00ednh n\u00e0y cho ph\u00e9p n\u00f3 x\u1eed l\u00fd c\u00e1c t\u1eadp d\u1eef li\u1ec7u l\u1edbn c\u00f3 th\u1ec3 kh\u00f4ng v\u1eeba ho\u00e0n to\u00e0n v\u1edbi b\u1ed9 nh\u1edb. Tuy nhi\u00ean, nhi\u1ec5u do l\u1ea5y m\u1eabu l\u00f4 nh\u1ecf c\u00f3 th\u1ec3 l\u00e0m cho qu\u00e1 tr\u00ecnh t\u1ed1i \u01b0u h\u00f3a b\u1ecb nhi\u1ec5u, d\u1eabn \u0111\u1ebfn s\u1ef1 bi\u1ebfn \u0111\u1ed9ng c\u1ee7a h\u00e0m m\u1ea5t m\u00e1t trong qu\u00e1 tr\u00ecnh hu\u1ea5n luy\u1ec7n.<\/p>\n<p>\u0110\u1ec3 kh\u1eafc ph\u1ee5c \u0111i\u1ec1u n\u00e0y, m\u1ed9t s\u1ed1 bi\u1ebfn th\u1ec3 c\u1ee7a SGD \u0111\u00e3 \u0111\u01b0\u1ee3c \u0111\u1ec1 xu\u1ea5t, ch\u1eb3ng h\u1ea1n nh\u01b0:<\/p>\n<ul>\n<li><strong>Gi\u1ea3m d\u1ea7n \u0111\u1ed9 d\u1ed1c h\u00e0ng lo\u1ea1t nh\u1ecf<\/strong>: N\u00f3 s\u1eed d\u1ee5ng m\u1ed9t lo\u1ea1t \u0111i\u1ec3m d\u1eef li\u1ec7u c\u00f3 k\u00edch th\u01b0\u1edbc c\u1ed1 \u0111\u1ecbnh nh\u1ecf trong m\u1ed7i l\u1ea7n l\u1eb7p, t\u1ea1o ra s\u1ef1 c\u00e2n b\u1eb1ng gi\u1eefa t\u00ednh \u1ed5n \u0111\u1ecbnh c\u1ee7a vi\u1ec7c gi\u1ea3m \u0111\u1ed9 d\u1ed1c h\u00e0ng lo\u1ea1t v\u00e0 hi\u1ec7u qu\u1ea3 t\u00ednh to\u00e1n c\u1ee7a SGD.<\/li>\n<li><strong>Gi\u1ea3m d\u1ea7n \u0111\u1ed9 d\u1ed1c tr\u1ef1c tuy\u1ebfn<\/strong>: N\u00f3 x\u1eed l\u00fd m\u1ed9t \u0111i\u1ec3m d\u1eef li\u1ec7u t\u1ea1i m\u1ed9t th\u1eddi \u0111i\u1ec3m, c\u1eadp nh\u1eadt c\u00e1c tham s\u1ed1 sau m\u1ed7i \u0111i\u1ec3m d\u1eef li\u1ec7u. C\u00e1ch ti\u1ebfp c\u1eadn n\u00e0y c\u00f3 th\u1ec3 r\u1ea5t kh\u00f4ng \u1ed5n \u0111\u1ecbnh nh\u01b0ng r\u1ea5t h\u1eefu \u00edch khi x\u1eed l\u00fd d\u1eef li\u1ec7u truy\u1ec1n tr\u1ef1c tuy\u1ebfn.<\/li>\n<\/ul>\n<h2>Ph\u00e2n t\u00edch c\u00e1c t\u00ednh n\u0103ng ch\u00ednh c\u1ee7a Gi\u1ea3m d\u1ea7n \u0111\u1ed9 d\u1ed1c ng\u1eabu nhi\u00ean<\/h2>\n<p>C\u00e1c t\u00ednh n\u0103ng ch\u00ednh c\u1ee7a Gi\u1ea3m d\u1ea7n \u0111\u1ed9 d\u1ed1c ng\u1eabu nhi\u00ean bao g\u1ed3m:<\/p>\n<ol>\n<li><strong>Hi\u1ec7u qu\u1ea3<\/strong>: SGD ch\u1ec9 x\u1eed l\u00fd m\u1ed9t t\u1eadp h\u1ee3p con d\u1eef li\u1ec7u nh\u1ecf trong m\u1ed7i l\u1ea7n l\u1eb7p, gi\u00fap t\u00ednh to\u00e1n hi\u1ec7u qu\u1ea3, \u0111\u1eb7c bi\u1ec7t \u0111\u1ed1i v\u1edbi c\u00e1c t\u1eadp d\u1eef li\u1ec7u l\u1edbn.<\/li>\n<li><strong>Kh\u1ea3 n\u0103ng m\u1edf r\u1ed9ng b\u1ed9 nh\u1edb<\/strong>: V\u00ec SGD ho\u1ea1t \u0111\u1ed9ng v\u1edbi c\u00e1c l\u00f4 nh\u1ecf n\u00ean n\u00f3 c\u00f3 th\u1ec3 x\u1eed l\u00fd c\u00e1c t\u1eadp d\u1eef li\u1ec7u kh\u00f4ng ho\u00e0n to\u00e0n n\u1eb1m g\u1ecdn trong b\u1ed9 nh\u1edb.<\/li>\n<li><strong>Ng\u1eabu nhi\u00ean<\/strong>: B\u1ea3n ch\u1ea5t ng\u1eabu nhi\u00ean c\u1ee7a SGD c\u00f3 th\u1ec3 gi\u00fap tho\u00e1t kh\u1ecfi m\u1ee9c t\u1ed1i thi\u1ec3u c\u1ee5c b\u1ed9 v\u00e0 tr\u00e1nh b\u1ecb m\u1eafc k\u1eb9t trong c\u00e1c \u0111i\u1ec3m \u1ed5n \u0111\u1ecbnh trong qu\u00e1 tr\u00ecnh t\u1ed1i \u01b0u h\u00f3a.<\/li>\n<li><strong>Ti\u1ebfng \u1ed3n<\/strong>: T\u00ednh ng\u1eabu nhi\u00ean do l\u1ea5y m\u1eabu l\u00f4 nh\u1ecf c\u00f3 th\u1ec3 g\u00e2y ra bi\u1ebfn \u0111\u1ed9ng trong h\u00e0m m\u1ea5t m\u00e1t, l\u00e0m cho qu\u00e1 tr\u00ecnh t\u1ed1i \u01b0u h\u00f3a b\u1ecb nhi\u1ec5u.<\/li>\n<\/ol>\n<h2>C\u00e1c ki\u1ec3u gi\u1ea3m d\u1ea7n \u0111\u1ed9 d\u1ed1c ng\u1eabu nhi\u00ean<\/h2>\n<p>C\u00f3 m\u1ed9t s\u1ed1 bi\u1ebfn th\u1ec3 c\u1ee7a Stochastic gradient Descent, m\u1ed7i bi\u1ebfn th\u1ec3 c\u00f3 nh\u1eefng \u0111\u1eb7c \u0111i\u1ec3m ri\u00eang. D\u01b0\u1edbi \u0111\u00e2y l\u00e0 m\u1ed9t s\u1ed1 lo\u1ea1i ph\u1ed5 bi\u1ebfn:<\/p>\n<table>\n<thead>\n<tr>\n<th>Ki\u1ec3u<\/th>\n<th>S\u1ef1 mi\u00eau t\u1ea3<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td>Gi\u1ea3m d\u1ea7n \u0111\u1ed9 d\u1ed1c h\u00e0ng lo\u1ea1t nh\u1ecf<\/td>\n<td>S\u1eed d\u1ee5ng m\u1ed9t lo\u1ea1t \u0111i\u1ec3m d\u1eef li\u1ec7u c\u00f3 k\u00edch th\u01b0\u1edbc c\u1ed1 \u0111\u1ecbnh nh\u1ecf trong m\u1ed7i l\u1ea7n l\u1eb7p.<\/td>\n<\/tr>\n<tr>\n<td>Gi\u1ea3m d\u1ea7n \u0111\u1ed9 d\u1ed1c tr\u1ef1c tuy\u1ebfn<\/td>\n<td>X\u1eed l\u00fd m\u1ed9t \u0111i\u1ec3m d\u1eef li\u1ec7u t\u1ea1i m\u1ed9t th\u1eddi \u0111i\u1ec3m, c\u1eadp nh\u1eadt c\u00e1c tham s\u1ed1 sau m\u1ed7i \u0111i\u1ec3m d\u1eef li\u1ec7u.<\/td>\n<\/tr>\n<tr>\n<td>\u0110\u1ed9ng l\u1ef1c SGD<\/td>\n<td>K\u1ebft h\u1ee3p \u0111\u1ed9ng l\u1ef1c \u0111\u1ec3 l\u00e0m tr\u01a1n tru qu\u00e1 tr\u00ecnh t\u1ed1i \u01b0u h\u00f3a v\u00e0 t\u0103ng t\u1ed1c \u0111\u1ed9 h\u1ed9i t\u1ee5.<\/td>\n<\/tr>\n<tr>\n<td>\u0110\u1ed9 d\u1ed1c t\u0103ng t\u1ed1c Nesterov (NAG)<\/td>\n<td>M\u1ed9t ph\u1ea7n m\u1edf r\u1ed9ng c\u1ee7a \u0111\u00e0 SGD gi\u00fap \u0111i\u1ec1u ch\u1ec9nh h\u01b0\u1edbng c\u1eadp nh\u1eadt \u0111\u1ec3 c\u00f3 hi\u1ec7u su\u1ea5t t\u1ed1t h\u01a1n.<\/td>\n<\/tr>\n<tr>\n<td>Adagrad<\/td>\n<td>\u0110i\u1ec1u ch\u1ec9nh t\u1ed1c \u0111\u1ed9 h\u1ecdc t\u1eadp cho t\u1eebng tham s\u1ed1 d\u1ef1a tr\u00ean \u0111\u1ed9 d\u1ed1c l\u1ecbch s\u1eed.<\/td>\n<\/tr>\n<tr>\n<td>RMSprop<\/td>\n<td>T\u01b0\u01a1ng t\u1ef1 nh\u01b0 Adagrad nh\u01b0ng s\u1eed d\u1ee5ng \u0111\u01b0\u1eddng trung b\u00ecnh \u0111\u1ed9ng c\u1ee7a b\u00ecnh ph\u01b0\u01a1ng gradient \u0111\u1ec3 \u0111i\u1ec1u ch\u1ec9nh t\u1ed1c \u0111\u1ed9 h\u1ecdc.<\/td>\n<\/tr>\n<tr>\n<td>Adam<\/td>\n<td>K\u1ebft h\u1ee3p l\u1ee3i \u00edch c\u1ee7a \u0111\u1ed9ng l\u01b0\u1ee3ng v\u00e0 RMSprop \u0111\u1ec3 \u0111\u1ea1t \u0111\u01b0\u1ee3c s\u1ef1 h\u1ed9i t\u1ee5 nhanh h\u01a1n.<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<h2>C\u00e1c c\u00e1ch s\u1eed d\u1ee5ng Gi\u1ea3m d\u1ea7n \u0111\u1ed9 d\u1ed1c ng\u1eabu nhi\u00ean, c\u00e1c v\u1ea5n \u0111\u1ec1 v\u00e0 gi\u1ea3i ph\u00e1p li\u00ean quan \u0111\u1ebfn vi\u1ec7c s\u1eed d\u1ee5ng<\/h2>\n<p>Gi\u1ea3m d\u1ea7n ng\u1eabu nhi\u00ean ng\u1eabu nhi\u00ean \u0111\u01b0\u1ee3c s\u1eed d\u1ee5ng r\u1ed9ng r\u00e3i trong c\u00e1c nhi\u1ec7m v\u1ee5 h\u1ecdc m\u00e1y kh\u00e1c nhau, \u0111\u1eb7c bi\u1ec7t l\u00e0 trong vi\u1ec7c \u0111\u00e0o t\u1ea1o m\u1ea1ng l\u01b0\u1edbi th\u1ea7n kinh s\u00e2u. N\u00f3 \u0111\u00e3 th\u00e0nh c\u00f4ng trong nhi\u1ec1u \u1ee9ng d\u1ee5ng nh\u1edd t\u00ednh hi\u1ec7u qu\u1ea3 v\u00e0 kh\u1ea3 n\u0103ng x\u1eed l\u00fd c\u00e1c t\u1eadp d\u1eef li\u1ec7u l\u1edbn. Tuy nhi\u00ean, s\u1eed d\u1ee5ng SGD hi\u1ec7u qu\u1ea3 c\u0169ng c\u00f3 nh\u1eefng th\u00e1ch th\u1ee9c:<\/p>\n<ol>\n<li>\n<p><strong>L\u1ef1a ch\u1ecdn t\u1ef7 l\u1ec7 h\u1ecdc t\u1eadp<\/strong>: Vi\u1ec7c l\u1ef1a ch\u1ecdn t\u1ed1c \u0111\u1ed9 h\u1ecdc th\u00edch h\u1ee3p l\u00e0 r\u1ea5t quan tr\u1ecdng cho s\u1ef1 h\u1ed9i t\u1ee5 c\u1ee7a SGD. T\u1ed1c \u0111\u1ed9 h\u1ecdc qu\u00e1 cao c\u00f3 th\u1ec3 khi\u1ebfn qu\u00e1 tr\u00ecnh t\u1ed1i \u01b0u h\u00f3a b\u1ecb ph\u00e2n k\u1ef3, trong khi t\u1ed1c \u0111\u1ed9 h\u1ecdc qu\u00e1 th\u1ea5p c\u00f3 th\u1ec3 d\u1eabn \u0111\u1ebfn h\u1ed9i t\u1ee5 ch\u1eadm. L\u1eadp k\u1ebf ho\u1ea1ch t\u1ed1c \u0111\u1ed9 h\u1ecdc t\u1eadp ho\u1eb7c thu\u1eadt to\u00e1n t\u1ed1c \u0111\u1ed9 h\u1ecdc th\u00edch \u1ee9ng c\u00f3 th\u1ec3 gi\u00fap gi\u1ea3m thi\u1ec3u v\u1ea5n \u0111\u1ec1 n\u00e0y.<\/p>\n<\/li>\n<li>\n<p><strong>Ti\u1ebfng \u1ed3n v\u00e0 bi\u1ebfn \u0111\u1ed9ng<\/strong>: B\u1ea3n ch\u1ea5t ng\u1eabu nhi\u00ean c\u1ee7a SGD t\u1ea1o ra nhi\u1ec5u, g\u00e2y ra bi\u1ebfn \u0111\u1ed9ng trong h\u00e0m loss trong qu\u00e1 tr\u00ecnh hu\u1ea5n luy\u1ec7n. \u0110i\u1ec1u n\u00e0y c\u00f3 th\u1ec3 g\u00e2y kh\u00f3 kh\u0103n cho vi\u1ec7c x\u00e1c \u0111\u1ecbnh li\u1ec7u qu\u00e1 tr\u00ecnh t\u1ed1i \u01b0u h\u00f3a c\u00f3 th\u1ef1c s\u1ef1 h\u1ed9i t\u1ee5 hay b\u1ecb m\u1eafc k\u1eb9t trong m\u1ed9t gi\u1ea3i ph\u00e1p d\u01b0\u1edbi m\u1ee9c t\u1ed1i \u01b0u hay kh\u00f4ng. \u0110\u1ec3 gi\u1ea3i quy\u1ebft v\u1ea5n \u0111\u1ec1 n\u00e0y, c\u00e1c nh\u00e0 nghi\u00ean c\u1ee9u th\u01b0\u1eddng theo d\u00f5i h\u00e0m m\u1ea5t m\u00e1t qua nhi\u1ec1u l\u1ea7n ch\u1ea1y ho\u1eb7c s\u1eed d\u1ee5ng t\u00ednh n\u0103ng d\u1eebng s\u1edbm d\u1ef1a tr\u00ean hi\u1ec7u su\u1ea5t x\u00e1c th\u1ef1c.<\/p>\n<\/li>\n<li>\n<p><strong>\u0110\u1ed9 d\u1ed1c bi\u1ebfn m\u1ea5t v\u00e0 b\u00f9ng n\u1ed5<\/strong>: Trong m\u1ea1ng l\u01b0\u1edbi th\u1ea7n kinh s\u00e2u, \u0111\u1ed9 d\u1ed1c c\u00f3 th\u1ec3 tr\u1edf n\u00ean nh\u1ecf \u0111\u1ebfn m\u1ee9c g\u1ea7n nh\u01b0 bi\u1ebfn m\u1ea5t ho\u1eb7c b\u00f9ng n\u1ed5 trong qu\u00e1 tr\u00ecnh hu\u1ea5n luy\u1ec7n, \u1ea3nh h\u01b0\u1edfng \u0111\u1ebfn vi\u1ec7c c\u1eadp nh\u1eadt tham s\u1ed1. C\u00e1c k\u1ef9 thu\u1eadt nh\u01b0 c\u1eaft gradient v\u00e0 chu\u1ea9n h\u00f3a h\u00e0ng lo\u1ea1t c\u00f3 th\u1ec3 gi\u00fap \u1ed5n \u0111\u1ecbnh qu\u00e1 tr\u00ecnh t\u1ed1i \u01b0u h\u00f3a.<\/p>\n<\/li>\n<li>\n<p><strong>\u0110i\u1ec3m y\u00ean ng\u1ef1a<\/strong>: SGD c\u00f3 th\u1ec3 b\u1ecb k\u1eb9t \u1edf c\u00e1c \u0111i\u1ec3m y\u00ean ng\u1ef1a, l\u00e0 c\u00e1c \u0111i\u1ec3m t\u1edbi h\u1ea1n c\u1ee7a h\u00e0m m\u1ea5t m\u00e1t trong \u0111\u00f3 m\u1ed9t s\u1ed1 h\u01b0\u1edbng c\u00f3 \u0111\u1ed9 cong d\u01b0\u01a1ng, trong khi c\u00e1c h\u01b0\u1edbng kh\u00e1c c\u00f3 \u0111\u1ed9 cong \u00e2m. S\u1eed d\u1ee5ng c\u00e1c bi\u1ebfn th\u1ec3 SGD d\u1ef1a tr\u00ean \u0111\u1ed9ng l\u01b0\u1ee3ng c\u00f3 th\u1ec3 gi\u00fap v\u01b0\u1ee3t qua \u0111i\u1ec3m y\u00ean ng\u1ef1a hi\u1ec7u qu\u1ea3 h\u01a1n.<\/p>\n<\/li>\n<\/ol>\n<h2>C\u00e1c \u0111\u1eb7c \u0111i\u1ec3m ch\u00ednh v\u00e0 so s\u00e1nh kh\u00e1c v\u1edbi c\u00e1c thu\u1eadt ng\u1eef t\u01b0\u01a1ng t\u1ef1<\/h2>\n<table>\n<thead>\n<tr>\n<th>\u0111\u1eb7c tr\u01b0ng<\/th>\n<th>Gi\u1ea3m d\u1ea7n \u0111\u1ed9 d\u1ed1c ng\u1eabu nhi\u00ean (SGD)<\/th>\n<th>Gi\u1ea3m d\u1ea7n \u0111\u1ed9 d\u1ed1c h\u00e0ng lo\u1ea1t<\/th>\n<th>Gi\u1ea3m d\u1ea7n \u0111\u1ed9 d\u1ed1c h\u00e0ng lo\u1ea1t nh\u1ecf<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td>X\u1eed l\u00ed d\u1eef li\u1ec7u<\/td>\n<td>L\u1ea5y m\u1eabu ng\u1eabu nhi\u00ean c\u00e1c l\u00f4 nh\u1ecf t\u1eeb d\u1eef li\u1ec7u hu\u1ea5n luy\u1ec7n.<\/td>\n<td>X\u1eed l\u00fd to\u00e0n b\u1ed9 t\u1eadp d\u1eef li\u1ec7u \u0111\u00e0o t\u1ea1o c\u00f9ng m\u1ed9t l\u00fac.<\/td>\n<td>L\u1ea5y m\u1eabu ng\u1eabu nhi\u00ean c\u00e1c l\u00f4 nh\u1ecf, s\u1ef1 th\u1ecfa hi\u1ec7p gi\u1eefa SGD v\u00e0 Batch GD.<\/td>\n<\/tr>\n<tr>\n<td>Hi\u1ec7u qu\u1ea3 t\u00ednh to\u00e1n<\/td>\n<td>Hi\u1ec7u qu\u1ea3 cao v\u00ec n\u00f3 ch\u1ec9 x\u1eed l\u00fd m\u1ed9t t\u1eadp h\u1ee3p con d\u1eef li\u1ec7u nh\u1ecf.<\/td>\n<td>\u00cdt hi\u1ec7u qu\u1ea3 h\u01a1n v\u00ec n\u00f3 x\u1eed l\u00fd to\u00e0n b\u1ed9 t\u1eadp d\u1eef li\u1ec7u.<\/td>\n<td>Hi\u1ec7u qu\u1ea3, nh\u01b0ng kh\u00f4ng b\u1eb1ng SGD thu\u1ea7n t\u00fay.<\/td>\n<\/tr>\n<tr>\n<td>Thu\u1ed9c t\u00ednh h\u1ed9i t\u1ee5<\/td>\n<td>C\u00f3 th\u1ec3 h\u1ed9i t\u1ee5 nhanh h\u01a1n do tho\u00e1t kh\u1ecfi c\u1ef1c ti\u1ec3u c\u1ee5c b\u1ed9.<\/td>\n<td>H\u1ed9i t\u1ee5 ch\u1eadm nh\u01b0ng \u1ed5n \u0111\u1ecbnh h\u01a1n.<\/td>\n<td>H\u1ed9i t\u1ee5 nhanh h\u01a1n Batch GD.<\/td>\n<\/tr>\n<tr>\n<td>Ti\u1ebfng \u1ed3n<\/td>\n<td>T\u1ea1o ra ti\u1ebfng \u1ed3n, d\u1eabn \u0111\u1ebfn s\u1ef1 bi\u1ebfn \u0111\u1ed9ng c\u1ee7a h\u00e0m m\u1ea5t m\u00e1t.<\/td>\n<td>Kh\u00f4ng c\u00f3 ti\u1ebfng \u1ed3n do s\u1eed d\u1ee5ng b\u1ed9 d\u1eef li\u1ec7u \u0111\u1ea7y \u0111\u1ee7.<\/td>\n<td>T\u1ea1o ra m\u1ed9t s\u1ed1 ti\u1ebfng \u1ed3n nh\u01b0ng \u00edt h\u01a1n SGD thu\u1ea7n t\u00fay.<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<h2>C\u00e1c quan \u0111i\u1ec3m v\u00e0 c\u00f4ng ngh\u1ec7 c\u1ee7a t\u01b0\u01a1ng lai li\u00ean quan \u0111\u1ebfn Gi\u1ea3m d\u1ea7n \u0111\u1ed9 d\u1ed1c ng\u1eabu nhi\u00ean<\/h2>\n<p>Gi\u1ea3m d\u1ea7n \u0111\u1ed9 d\u1ed1c ng\u1eabu nhi\u00ean ti\u1ebfp t\u1ee5c l\u00e0 m\u1ed9t thu\u1eadt to\u00e1n t\u1ed1i \u01b0u h\u00f3a c\u01a1 b\u1ea3n trong h\u1ecdc m\u00e1y v\u00e0 d\u1ef1 ki\u1ebfn s\u1ebd \u0111\u00f3ng m\u1ed9t vai tr\u00f2 quan tr\u1ecdng trong t\u01b0\u01a1ng lai. C\u00e1c nh\u00e0 nghi\u00ean c\u1ee9u \u0111ang li\u00ean t\u1ee5c kh\u00e1m ph\u00e1 c\u00e1c s\u1eeda \u0111\u1ed5i v\u00e0 c\u1ea3i ti\u1ebfn \u0111\u1ec3 n\u00e2ng cao hi\u1ec7u su\u1ea5t v\u00e0 \u0111\u1ed9 \u1ed5n \u0111\u1ecbnh c\u1ee7a n\u00f3. M\u1ed9t s\u1ed1 ph\u00e1t tri\u1ec3n ti\u1ec1m n\u0103ng trong t\u01b0\u01a1ng lai bao g\u1ed3m:<\/p>\n<ol>\n<li>\n<p><strong>T\u1ef7 l\u1ec7 h\u1ecdc t\u1eadp th\u00edch \u1ee9ng<\/strong>: C\u00e1c thu\u1eadt to\u00e1n t\u1ed1c \u0111\u1ed9 h\u1ecdc th\u00edch \u1ee9ng ph\u1ee9c t\u1ea1p h\u01a1n c\u00f3 th\u1ec3 \u0111\u01b0\u1ee3c ph\u00e1t tri\u1ec3n \u0111\u1ec3 x\u1eed l\u00fd hi\u1ec7u qu\u1ea3 nhi\u1ec1u v\u1ea5n \u0111\u1ec1 t\u1ed1i \u01b0u h\u00f3a h\u01a1n.<\/p>\n<\/li>\n<li>\n<p><strong>Song song h\u00f3a<\/strong>: Vi\u1ec7c song song h\u00f3a SGD \u0111\u1ec3 t\u1eadn d\u1ee5ng nhi\u1ec1u b\u1ed9 x\u1eed l\u00fd ho\u1eb7c h\u1ec7 th\u1ed1ng m\u00e1y t\u00ednh ph\u00e2n t\u00e1n c\u00f3 th\u1ec3 t\u0103ng t\u1ed1c \u0111\u00e1ng k\u1ec3 th\u1eddi gian \u0111\u00e0o t\u1ea1o cho c\u00e1c m\u00f4 h\u00ecnh quy m\u00f4 l\u1edbn.<\/p>\n<\/li>\n<li>\n<p><strong>K\u1ef9 thu\u1eadt t\u0103ng t\u1ed1c<\/strong>: C\u00e1c k\u1ef9 thu\u1eadt nh\u01b0 \u0111\u1ed9ng l\u01b0\u1ee3ng, gia t\u1ed1c Nesterov v\u00e0 ph\u01b0\u01a1ng ph\u00e1p gi\u1ea3m ph\u01b0\u01a1ng sai c\u00f3 th\u1ec3 \u0111\u01b0\u1ee3c c\u1ea3i ti\u1ebfn th\u00eam \u0111\u1ec3 c\u1ea3i thi\u1ec7n t\u1ed1c \u0111\u1ed9 h\u1ed9i t\u1ee5.<\/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 Gi\u1ea3m d\u1ea7n \u0111\u1ed9 d\u1ed1c ng\u1eabu nhi\u00ean<\/h2>\n<p>M\u00e1y ch\u1ee7 proxy \u0111\u00f3ng vai tr\u00f2 trung gian gi\u1eefa m\u00e1y kh\u00e1ch v\u00e0 m\u00e1y ch\u1ee7 kh\u00e1c tr\u00ean internet. M\u1eb7c d\u00f9 ch\u00fang kh\u00f4ng li\u00ean quan tr\u1ef1c ti\u1ebfp \u0111\u1ebfn Gi\u1ea3m d\u1ea7n \u0111\u1ed9 d\u1ed1c ng\u1eabu nhi\u00ean nh\u01b0ng ch\u00fang c\u00f3 th\u1ec3 ph\u00f9 h\u1ee3p trong c\u00e1c t\u00ecnh hu\u1ed1ng c\u1ee5 th\u1ec3. V\u00ed d\u1ee5:<\/p>\n<ol>\n<li>\n<p><strong>Quy\u1ec1n ri\u00eang t\u01b0 d\u1eef li\u1ec7u<\/strong>: Khi \u0111\u00e0o t\u1ea1o c\u00e1c m\u00f4 h\u00ecnh machine learning tr\u00ean c\u00e1c t\u1eadp d\u1eef li\u1ec7u nh\u1ea1y c\u1ea3m ho\u1eb7c \u0111\u1ed9c quy\u1ec1n, m\u00e1y ch\u1ee7 proxy c\u00f3 th\u1ec3 \u0111\u01b0\u1ee3c s\u1eed d\u1ee5ng \u0111\u1ec3 \u1ea9n danh d\u1eef li\u1ec7u, b\u1ea3o v\u1ec7 quy\u1ec1n ri\u00eang t\u01b0 c\u1ee7a ng\u01b0\u1eddi d\u00f9ng.<\/p>\n<\/li>\n<li>\n<p><strong>C\u00e2n b\u1eb1ng t\u1ea3i<\/strong>: Trong c\u00e1c h\u1ec7 th\u1ed1ng m\u00e1y h\u1ecdc ph\u00e2n t\u00e1n, 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 kh\u1ed1i l\u01b0\u1ee3ng c\u00f4ng vi\u1ec7c t\u00ednh to\u00e1n m\u1ed9t c\u00e1ch hi\u1ec7u qu\u1ea3.<\/p>\n<\/li>\n<li>\n<p><strong>B\u1ed9 nh\u1edb \u0111\u1ec7m<\/strong>: M\u00e1y ch\u1ee7 proxy c\u00f3 th\u1ec3 l\u01b0u v\u00e0o b\u1ed9 \u0111\u1ec7m c\u00e1c t\u00e0i nguy\u00ean \u0111\u01b0\u1ee3c truy c\u1eadp th\u01b0\u1eddng xuy\u00ean, bao g\u1ed3m c\u00e1c l\u00f4 d\u1eef li\u1ec7u nh\u1ecf, c\u00f3 th\u1ec3 c\u1ea3i thi\u1ec7n th\u1eddi gian truy c\u1eadp d\u1eef li\u1ec7u trong qu\u00e1 tr\u00ecnh \u0111\u00e0o t\u1ea1o.<\/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 Gi\u1ea3m d\u1ea7n \u0111\u1ed9 d\u1ed1c ng\u1eabu nhi\u00ean, b\u1ea1n c\u00f3 th\u1ec3 tham kh\u1ea3o c\u00e1c t\u00e0i nguy\u00ean sau:<\/p>\n<ol>\n<li><a href=\"http:\/\/cs231n.github.io\/optimization-1\/\" target=\"_new\" rel=\"noopener nofollow\">B\u00e0i gi\u1ea3ng CS231n c\u1ee7a \u0110\u1ea1i h\u1ecdc Stanford v\u1ec1 ph\u01b0\u01a1ng ph\u00e1p t\u1ed1i \u01b0u h\u00f3a<\/a><\/li>\n<li><a href=\"https:\/\/www.deeplearningbook.org\/contents\/optimization.html\" target=\"_new\" rel=\"noopener nofollow\">S\u00e1ch Deep Learning \u2013 Ch\u01b0\u01a1ng 8: T\u1ed1i \u01b0u h\u00f3a \u0111\u1ec3 \u0111\u00e0o t\u1ea1o c\u00e1c m\u00f4 h\u00ecnh s\u00e2u<\/a><\/li>\n<\/ol>\n<p>H\u00e3y nh\u1edb kh\u00e1m ph\u00e1 nh\u1eefng ngu\u1ed3n n\u00e0y \u0111\u1ec3 hi\u1ec3u s\u00e2u h\u01a1n v\u1ec1 c\u00e1c kh\u00e1i ni\u1ec7m v\u00e0 \u1ee9ng d\u1ee5ng c\u1ee7a Gi\u1ea3m d\u1ea7n \u0111\u1ed9 d\u1ed1c ng\u1eabu nhi\u00ean.<\/p>","protected":false},"featured_media":470609,"menu_order":0,"template":"","meta":{"_acf_changed":false,"content-type":"","inline_featured_image":false,"footnotes":""},"class_list":["post-479160","wiki","type-wiki","status-publish","has-post-thumbnail","hentry"],"acf":{"faq_title":"Frequently Asked Questions about <mark>Stochastic Gradient Descent: An In-depth Analysis<\/mark>","faq_items":[{"question":"What is Stochastic Gradient Descent (SGD)?","answer":"<p>Stochastic Gradient Descent (SGD) is an optimization algorithm used in machine learning and deep learning to find the optimal parameters of a model by iteratively updating them based on mini-batches of training data. It introduces randomness in the parameter updates, making it computationally efficient and capable of handling large datasets.<\/p>"},{"question":"How does Stochastic Gradient Descent work?","answer":"<p>SGD works by randomly sampling mini-batches of data from the training set and computing the gradient of the loss function with respect to the model parameters on these mini-batches. The parameters are then updated using the computed gradient and a learning rate, which controls the step size of the updates. This process is repeated iteratively until the convergence criteria are met.<\/p>"},{"question":"What are the key features of Stochastic Gradient Descent?","answer":"<p>The key features of SGD include its efficiency, memory scalability, and ability to escape local minima due to the randomness introduced by mini-batch sampling. However, it can also introduce noise in the optimization process, leading to fluctuations in the loss function during training.<\/p>"},{"question":"What types of Stochastic Gradient Descent exist?","answer":"<p>Several variants of Stochastic Gradient Descent have been developed, including:<\/p><ul><li>Mini-batch Gradient Descent: Uses a fixed-size batch of data points in each iteration.<\/li><li>Online Gradient Descent: Processes one data point at a time.<\/li><li>Momentum SGD: Incorporates momentum to accelerate convergence.<\/li><li>Nesterov Accelerated Gradient (NAG): Adjusts the update direction for better performance.<\/li><li>Adagrad and RMSprop: Adaptive learning rate algorithms.<\/li><li>Adam: Combines benefits of momentum and RMSprop for faster convergence.<\/li><\/ul>"},{"question":"How can Stochastic Gradient Descent be used, and what are the challenges?","answer":"<p>SGD is widely used in machine learning tasks, particularly in training deep neural networks. However, using SGD effectively comes with challenges, such as selecting an appropriate learning rate, dealing with noise and fluctuations, handling vanishing and exploding gradients, and addressing saddle points.<\/p>"},{"question":"What are the future perspectives of Stochastic Gradient Descent?","answer":"<p>In the future, researchers are expected to explore improvements in adaptive learning rates, parallelization, and acceleration techniques to further enhance the performance and stability of SGD in machine learning applications.<\/p>"},{"question":"How are proxy servers associated with Stochastic Gradient Descent?","answer":"<p>Proxy servers can be relevant in scenarios involving data privacy, load balancing in distributed systems, and caching frequently accessed resources like mini-batches during SGD training. They can complement the use of SGD in specific machine learning setups.<\/p>"}]},"_links":{"self":[{"href":"https:\/\/oneproxy.pro\/vn\/wp-json\/wp\/v2\/wiki\/479160","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\/479160\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/oneproxy.pro\/vn\/wp-json\/wp\/v2\/media\/470609"}],"wp:attachment":[{"href":"https:\/\/oneproxy.pro\/vn\/wp-json\/wp\/v2\/media?parent=479160"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}