{"id":215825,"date":"2023-10-02T15:03:43","date_gmt":"2023-10-02T15:03:43","guid":{"rendered":"https:\/\/datascientest.com\/?p=215825"},"modified":"2024-05-16T15:30:05","modified_gmt":"2024-05-16T15:30:05","slug":"long-short-term-memory-tout-savoir","status":"publish","type":"post","link":"https:\/\/datascientest.com\/long-short-term-memory-tout-savoir","title":{"rendered":"Long Short Term Memory (LSTM) : de quoi s\u2019agit-il ?"},"content":{"rendered":"\t\t<div data-elementor-type=\"wp-post\" data-elementor-id=\"215825\" class=\"elementor elementor-215825\" data-elementor-post-type=\"post\">\n\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-8d32c52 e-flex e-con-boxed e-con\" data-id=\"8d32c52\" data-element_type=\"container\" data-settings=\"{&quot;content_width&quot;:&quot;boxed&quot;}\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-addbdca elementor-widget elementor-widget-heading\" data-id=\"addbdca\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<style>\/*! elementor - v3.15.0 - 02-08-2023 *\/\n.elementor-heading-title{padding:0;margin:0;line-height:1}.elementor-widget-heading .elementor-heading-title[class*=elementor-size-]>a{color:inherit;font-size:inherit;line-height:inherit}.elementor-widget-heading .elementor-heading-title.elementor-size-small{font-size:15px}.elementor-widget-heading .elementor-heading-title.elementor-size-medium{font-size:19px}.elementor-widget-heading .elementor-heading-title.elementor-size-large{font-size:29px}.elementor-widget-heading .elementor-heading-title.elementor-size-xl{font-size:39px}.elementor-widget-heading .elementor-heading-title.elementor-size-xxl{font-size:59px}<\/style><h2 class=\"elementor-heading-title elementor-size-default\">Les r\u00e9seaux LSTM sont une variation des r\u00e9seaux de neurones r\u00e9currents (RNN). Ils ont \u00e9t\u00e9 propos\u00e9s par J\u00fcrgen Schmidhuber et son \u00e9quipe au d\u00e9but des ann\u00e9es 1990.  Alors que les RNN classiques ont des difficult\u00e9s \u00e0 traiter les d\u00e9pendances \u00e0 long terme en raison du probl\u00e8me du \"vanishing gradient\", les LSTM ont \u00e9t\u00e9 sp\u00e9cialement con\u00e7us pour surmonter ce probl\u00e8me.<\/h2>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-f861c28 elementor-widget elementor-widget-text-editor\" data-id=\"f861c28\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<style>\/*! elementor - v3.15.0 - 02-08-2023 *\/\n.elementor-widget-text-editor.elementor-drop-cap-view-stacked .elementor-drop-cap{background-color:#69727d;color:#fff}.elementor-widget-text-editor.elementor-drop-cap-view-framed .elementor-drop-cap{color:#69727d;border:3px solid;background-color:transparent}.elementor-widget-text-editor:not(.elementor-drop-cap-view-default) .elementor-drop-cap{margin-top:8px}.elementor-widget-text-editor:not(.elementor-drop-cap-view-default) .elementor-drop-cap-letter{width:1em;height:1em}.elementor-widget-text-editor .elementor-drop-cap{float:left;text-align:center;line-height:1;font-size:50px}.elementor-widget-text-editor .elementor-drop-cap-letter{display:inline-block}<\/style>\t\t\t\t<p><span style=\"font-weight: 400;\">Si vous \u00eates un adepte de notre blog, vous savez d\u00e9j\u00e0 ce que sont les <\/span><b>Recurrent Neural Networks (RNN)<\/b><span style=\"font-weight: 400;\">, si ce n\u2019est pas le cas n\u2019h\u00e9sitez pas \u00e0 lire <a href=\"https:\/\/datascientest.com\/recurrent-neural-network\">cet article<\/a> avant.<\/span><\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-6df7eaf e-flex e-con-boxed e-con\" data-id=\"6df7eaf\" data-element_type=\"container\" data-settings=\"{&quot;content_width&quot;:&quot;boxed&quot;}\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-a4ff110 elementor-widget elementor-widget-heading\" data-id=\"a4ff110\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">Le probl\u00e8me des RNN<\/h3>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-23c004a elementor-widget elementor-widget-text-editor\" data-id=\"23c004a\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p><span style=\"font-weight: 400;\">Le probl\u00e8me du <\/span><b>\u00ab\u00a0vanishing gradient\u00a0\u00bb<\/b><span style=\"font-weight: 400;\"> est un d\u00e9fi couramment rencontr\u00e9 lors de l&rsquo;entra\u00eenement des <a href=\"https:\/\/datascientest.com\/recurrent-neural-network\">r\u00e9seaux de neurones r\u00e9currents (RNN)<\/a>. Il se produit lorsque les RNN tentent de mod\u00e9liser des <\/span><b>d\u00e9pendances \u00e0 long terme<\/b><span style=\"font-weight: 400;\"> dans des s\u00e9quences. \u00c0 mesure que les gradients sont propag\u00e9s lors de la r\u00e9tropropagation, leur norme a tendance \u00e0 diminuer de mani\u00e8re exponentielle, ce qui entra\u00eene une mise \u00e0 jour des poids n\u00e9gligeable pour les neurones proches du d\u00e9but de la s\u00e9quence. En cons\u00e9quence, les RNN ont du mal \u00e0 apprendre et \u00e0 capturer des d\u00e9pendances \u00e0 long terme. <\/span><\/p><p><span style=\"font-weight: 400;\">Ce probl\u00e8me a conduit au d\u00e9veloppement des <\/span><b>r\u00e9seaux LSTM<\/b><span style=\"font-weight: 400;\">, qui utilisent des <\/span><b>m\u00e9canismes de r\u00e9gulation<\/b><span style=\"font-weight: 400;\"> pour \u00e9viter le vanishing gradient et capturer efficacement les d\u00e9pendances \u00e0 long terme dans les s\u00e9quences.<\/span><\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-ac4a84b e-flex e-con-boxed e-con\" data-id=\"ac4a84b\" data-element_type=\"container\" data-settings=\"{&quot;content_width&quot;:&quot;boxed&quot;}\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-3523161 elementor-widget elementor-widget-heading\" data-id=\"3523161\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">L\u2019architecture des LSTM<\/h3>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-cf8e9b0 elementor-widget elementor-widget-text-editor\" data-id=\"cf8e9b0\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p><span style=\"font-weight: 400;\">Les LSTM introduisent une nouvelle unit\u00e9 appel\u00e9e <\/span><b>cellule m\u00e9moire<\/b><span style=\"font-weight: 400;\">, qui permet au r\u00e9seau de stocker et d&rsquo;acc\u00e9der \u00e0 des informations sur une p\u00e9riode \u00e9tendue.<\/span><\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-bb6a38f elementor-widget elementor-widget-image\" data-id=\"bb6a38f\" data-element_type=\"widget\" data-widget_type=\"image.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<style>\/*! elementor - v3.15.0 - 02-08-2023 *\/\n.elementor-widget-image{text-align:center}.elementor-widget-image a{display:inline-block}.elementor-widget-image a img[src$=\".svg\"]{width:48px}.elementor-widget-image img{vertical-align:middle;display:inline-block}<\/style>\t\t\t\t\t\t\t\t\t<figure class=\"wp-caption\">\n\t\t\t\t\t\t\t\t\t\t<img decoding=\"async\" width=\"504\" height=\"419\" src=\"https:\/\/datascientest.com\/wp-content\/uploads\/2023\/10\/image1.png\" class=\"attachment-full size-full wp-image-215828\" alt=\"\" srcset=\"https:\/\/datascientest.com\/wp-content\/uploads\/2023\/10\/image1.png 504w, https:\/\/datascientest.com\/wp-content\/uploads\/2023\/10\/image1-300x249.png 300w\" sizes=\"(max-width: 504px) 100vw, 504px\" \/>\t\t\t\t\t\t\t\t\t\t\t<figcaption class=\"widget-image-caption wp-caption-text\">Sch\u00e9ma d\u2019une cellule m\u00e9moire d\u2019un LSTM<\/figcaption>\n\t\t\t\t\t\t\t\t\t\t<\/figure>\n\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-f6bf95f elementor-widget elementor-widget-text-editor\" data-id=\"f6bf95f\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p><span style=\"font-weight: 400;\">La cellule m\u00e9moire d&rsquo;un LSTM est compos\u00e9e de <\/span><b>plusieurs portes : une porte d&rsquo;entr\u00e9e, une porte de sortie et une porte d&rsquo;oubli.<\/b><span style=\"font-weight: 400;\"> Ces portes r\u00e9gulent le flux d&rsquo;informations \u00e0 l&rsquo;int\u00e9rieur de la cellule m\u00e9moire, permettant ainsi de <\/span><b>contr\u00f4ler les informations \u00e0 retenir et celles \u00e0 oublier<\/b><span style=\"font-weight: 400;\">. Cela donne aux LSTM la capacit\u00e9 de m\u00e9moriser des informations importantes sur de longues s\u00e9quences et d&rsquo;ignorer les \u00e9l\u00e9ments moins pertinents. <\/span><b><i>h_t<\/i><\/b><span style=\"font-weight: 400;\"> est l\u2019\u00e9tat cach\u00e9 habituel des RNN mais dans les r\u00e9seaux LSTM nous ajoutons un deuxi\u00e8me \u00e9tat appel\u00e9 <\/span><b><i>c_t<\/i><\/b><span style=\"font-weight: 400;\">. Ici, h_t repr\u00e9sente la m\u00e9moire courte du neurone et c_t repr\u00e9sente la m\u00e9moire \u00e0 long terme.\u00a0<\/span><\/p><p><span style=\"font-weight: 400;\">La porte d&rsquo;entr\u00e9e (1) d\u00e9termine quelles informations doivent \u00eatre mises \u00e0 jour dans la cellule m\u00e9moire. Elle prend en compte l&rsquo;entr\u00e9e actuelle <\/span><b><i>x_t<\/i><\/b><span style=\"font-weight: 400;\"> ainsi que l&rsquo;\u00e9tat pr\u00e9c\u00e9dent de la cellule m\u00e9moire h_(t-1) et g\u00e9n\u00e8re <\/span><b>un vecteur d&rsquo;activation<\/b><span style=\"font-weight: 400;\"> qui repr\u00e9sente les informations \u00e0 ajouter \u00e0 la cellule c_(t-1). Cet ajout d\u2019informations se traduit par une op\u00e9ration math\u00e9matique effectu\u00e9e entre ce vecteur d\u2019activation et l\u2019\u00e9tat pr\u00e9c\u00e9dent c_(t-1).\u00a0<\/span><\/p><p><span style=\"font-weight: 400;\">La porte d&rsquo;oubli (2) permet au LSTM de supprimer les informations obsol\u00e8tes de la cellule m\u00e9moire. Elle utilise \u00e0 la fois l&rsquo;entr\u00e9e actuelle et l&rsquo;\u00e9tat pr\u00e9c\u00e9dent pour g\u00e9n\u00e9rer un vecteur d&rsquo;activation qui d\u00e9termine quelles informations doivent \u00eatre oubli\u00e9es. Cela se traduit \u00e9galement par une op\u00e9ration math\u00e9matique effectu\u00e9e entre ce deuxi\u00e8me vecteur d\u2019activation et l\u2019\u00e9tat pr\u00e9c\u00e9dent c_(t-1). C\u2019est \u00e0 partir des deux op\u00e9rations pr\u00e9c\u00e9dentes sur c_(t-1) que l\u2019on obtient l\u2019\u00e9tat actuel c_t.\u00a0<\/span><\/p><p><span style=\"font-weight: 400;\">Enfin, la porte de sortie (3) d\u00e9termine la sortie du LSTM \u00e0 un instant donn\u00e9. Elle utilise l&rsquo;entr\u00e9e actuelle x_t et l&rsquo;\u00e9tat actuel de la cellule m\u00e9moire c_t pour g\u00e9n\u00e9rer un vecteur d&rsquo;activation qui repr\u00e9sente la sortie du LSTM. C\u2019est ainsi que l\u2019on obtient h_t.\u00a0<\/span><\/p><p><span style=\"font-weight: 400;\">La combinaison de ces trois portes permet au r\u00e9seau LSTM de g\u00e9rer efficacement les d\u00e9pendances \u00e0 long terme. Lors de la r\u00e9tropropagation du gradient, les LSTM peuvent maintenir <\/span><b>un flux d&rsquo;informations constant \u00e0 travers le temps<\/b><span style=\"font-weight: 400;\">, \u00e9vitant ainsi le probl\u00e8me du \u00ab\u00a0vanishing gradient\u00a0\u00bb et permettant un apprentissage plus stable et plus pr\u00e9cis.<\/span><\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-23546cd elementor-align-center button-principale elementor-widget elementor-widget-button\" data-id=\"23546cd\" data-element_type=\"widget\" data-widget_type=\"button.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<div class=\"elementor-button-wrapper\">\n\t\t\t<a class=\"elementor-button elementor-button-link elementor-size-sm elementor-animation-grow\" href=\"https:\/\/datascientest.com\/formation-data-scientist\">\n\t\t\t\t\t\t<span class=\"elementor-button-content-wrapper\">\n\t\t\t\t\t\t<span class=\"elementor-button-text\">Devenir un expert des LSTM<\/span>\n\t\t<\/span>\n\t\t\t\t\t<\/a>\n\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-ed9d272 e-flex e-con-boxed e-con\" data-id=\"ed9d272\" data-element_type=\"container\" data-settings=\"{&quot;content_width&quot;:&quot;boxed&quot;}\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-f21b2ca elementor-widget elementor-widget-heading\" data-id=\"f21b2ca\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">Concr\u00e8tement, \u00e0 quoi cela sert ?<\/h3>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-45fd520 elementor-widget elementor-widget-text-editor\" data-id=\"45fd520\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p><span style=\"font-weight: 400;\">Une des applications les plus courantes des r\u00e9seaux LSTM est la <\/span><b>traduction automatique.<\/b><span style=\"font-weight: 400;\"> Gr\u00e2ce \u00e0 leur capacit\u00e9 \u00e0 prendre en compte le contexte global d&rsquo;une s\u00e9quence, les LSTM peuvent capturer les d\u00e9pendances \u00e0 long terme entre les mots et g\u00e9n\u00e9rer des traductions plus pr\u00e9cises. De plus, les LSTM peuvent \u00e9galement \u00eatre utilis\u00e9s pour <\/span><b>g\u00e9n\u00e9rer du texte<\/b><span style=\"font-weight: 400;\"> de mani\u00e8re cr\u00e9ative, en apprenant \u00e0 partir de grands corpus de donn\u00e9es et en g\u00e9n\u00e9rant de nouvelles phrases coh\u00e9rentes et pertinentes.\u00a0<\/span><\/p><p><span style=\"font-weight: 400;\">Outre cela, les r\u00e9seaux LSTM ont \u00e9galement fait leurs preuves dans des domaines tels que <\/span><b>la reconnaissance de la parole<\/b><span style=\"font-weight: 400;\">. Gr\u00e2ce \u00e0 leur capacit\u00e9 \u00e0 mod\u00e9liser les d\u00e9pendances \u00e0 long terme, les LSTM peuvent capturer les structures temporelles complexes pr\u00e9sentes dans les signaux audios et produire des transcriptions pr\u00e9cises. Cela a des implications significatives dans des domaines tels que la transcription m\u00e9dicale, l&rsquo;assistance vocale et m\u00eame la commande vocale des appareils \u00e9lectroniques.\u00a0<\/span><\/p><p><span style=\"font-weight: 400;\">Les LSTM sont \u00e9galement utilis\u00e9s dans des t\u00e2ches de pr\u00e9diction et de classification de s\u00e9quences, telles que<\/span><b> la pr\u00e9diction de s\u00e9ries temporelles, l&rsquo;analyse de sentiments dans les textes, la d\u00e9tection d&rsquo;anomalies, et bien d&rsquo;autres encore.<\/b><span style=\"font-weight: 400;\"> Leur capacit\u00e9 \u00e0 mod\u00e9liser les d\u00e9pendances \u00e0 long terme et \u00e0 apprendre des motifs complexes les rend extr\u00eamement polyvalents et adapt\u00e9s \u00e0 une large gamme de probl\u00e8mes.<\/span><\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-0b27518 elementor-widget elementor-widget-image\" data-id=\"0b27518\" data-element_type=\"widget\" data-widget_type=\"image.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<img decoding=\"async\" width=\"900\" height=\"500\" src=\"https:\/\/datascientest.com\/wp-content\/uploads\/2023\/10\/LSTM.jpg\" class=\"attachment-full size-full wp-image-215832\" alt=\"\" srcset=\"https:\/\/datascientest.com\/wp-content\/uploads\/2023\/10\/LSTM.jpg 900w, https:\/\/datascientest.com\/wp-content\/uploads\/2023\/10\/LSTM-300x167.jpg 300w, https:\/\/datascientest.com\/wp-content\/uploads\/2023\/10\/LSTM-768x427.jpg 768w\" sizes=\"(max-width: 900px) 100vw, 900px\" \/>\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-cb67aa7 elementor-align-center button-principale elementor-widget elementor-widget-button\" data-id=\"cb67aa7\" data-element_type=\"widget\" data-widget_type=\"button.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<div class=\"elementor-button-wrapper\">\n\t\t\t<a class=\"elementor-button elementor-button-link elementor-size-sm elementor-animation-grow\" href=\"\/formation-data-scientist\">\n\t\t\t\t\t\t<span class=\"elementor-button-content-wrapper\">\n\t\t\t\t\t\t<span class=\"elementor-button-text\">Devenir Data Scientist<\/span>\n\t\t<\/span>\n\t\t\t\t\t<\/a>\n\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-068a5db e-flex e-con-boxed e-con\" data-id=\"068a5db\" data-element_type=\"container\" data-settings=\"{&quot;content_width&quot;:&quot;boxed&quot;}\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-813fa97 elementor-widget elementor-widget-heading\" data-id=\"813fa97\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">Mais ils ne sont pas parfaits<\/h3>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-2c17795 elementor-widget elementor-widget-text-editor\" data-id=\"2c17795\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p><span style=\"font-weight: 400;\">Cependant, malgr\u00e9 leur puissance et leur flexibilit\u00e9, les r\u00e9seaux LSTM ne sont pas d\u00e9pourvus de d\u00e9fis. Tout d&rsquo;abord, ils sont <\/span><b>plus complexes \u00e0 entra\u00eener<\/b><span style=\"font-weight: 400;\"> que les r\u00e9seaux de neurones traditionnels en raison du nombre accru de param\u00e8tres. Ils n\u00e9cessitent souvent <\/span><b>plus de donn\u00e9es d&rsquo;entra\u00eenement et de puissance de calcul<\/b><span style=\"font-weight: 400;\"> pour obtenir des r\u00e9sultats optimaux. De plus, les LSTM peuvent \u00eatre <\/span><b>sensibles au surapprentissage<\/b><span style=\"font-weight: 400;\"> si les donn\u00e9es d&rsquo;entra\u00eenement ne sont pas suffisamment vari\u00e9es et repr\u00e9sentatives.\u00a0<\/span><\/p><p><span style=\"font-weight: 400;\">N\u00e9anmoins, les r\u00e9seaux LSTM continuent de susciter un int\u00e9r\u00eat consid\u00e9rable dans la communaut\u00e9 de l&rsquo;apprentissage en profondeur, et <\/span><b>de nombreuses recherches sont en cours pour am\u00e9liorer leur efficacit\u00e9 et leur performance.<\/b><span style=\"font-weight: 400;\"> Des variantes telles que les r\u00e9seaux <\/span><b>LSTM bidirectionnels, les r\u00e9seaux LSTM empil\u00e9s et les r\u00e9seaux LSTM avec attention<\/b><span style=\"font-weight: 400;\"> sont d\u00e9velopp\u00e9es pour r\u00e9pondre \u00e0 des besoins sp\u00e9cifiques et obtenir des r\u00e9sultats encore meilleurs sur certaines t\u00e2ches.<\/span><\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-c604aa7 e-flex e-con-boxed e-con\" data-id=\"c604aa7\" data-element_type=\"container\" data-settings=\"{&quot;content_width&quot;:&quot;boxed&quot;}\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-ee9886e elementor-widget elementor-widget-text-editor\" data-id=\"ee9886e\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p>En conclusion,<span style=\"font-weight: 400;\"> les r\u00e9seaux LSTM ont r\u00e9volutionn\u00e9 le domaine de l&rsquo;apprentissage en profondeur en permettant de traiter efficacement les d\u00e9pendances \u00e0 long terme dans les donn\u00e9es s\u00e9quentielles. Leur capacit\u00e9 \u00e0 m\u00e9moriser et \u00e0 utiliser des informations sur de longues p\u00e9riodes de temps ouvre de nouvelles perspectives dans de nombreux domaines d&rsquo;application. Que ce soit pour la traduction automatique, la reconnaissance de la parole, l&rsquo;analyse de s\u00e9quences ou d&rsquo;autres t\u00e2ches, <strong>les r\u00e9seaux LSTM offrent une approche puissante et prometteuse pour mod\u00e9liser des donn\u00e9es complexes et capturer des motifs temporels<\/strong>. \u00c0 mesure que la recherche se poursuit, il est fort probable que les r\u00e9seaux LSTM continueront d&rsquo;\u00e9voluer et de jouer un r\u00f4le essentiel dans les futures avanc\u00e9es de l&rsquo;intelligence artificielle et de l&rsquo;apprentissage automatique.<\/span><\/p><p><span style=\"font-weight: 400;\">Par ailleurs, si vous souhaitez mettre en pratique les LSTM et aller plus loin en comprenant par exemple les r\u00e9seaux LSTM avec m\u00e9canismes d\u2019attention, n\u2019h\u00e9sitez pas \u00e0 rejoindre <\/span><a href=\"\/formation-data-scientist\" target=\"_blank\" rel=\"noopener\">notre formation Data Scientist<\/a>.<\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-1cf1779 elementor-align-center button-principale elementor-widget elementor-widget-button\" data-id=\"1cf1779\" data-element_type=\"widget\" data-widget_type=\"button.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<div class=\"elementor-button-wrapper\">\n\t\t\t<a class=\"elementor-button elementor-button-link elementor-size-sm elementor-animation-grow\" href=\"\/formations-data\">\n\t\t\t\t\t\t<span class=\"elementor-button-content-wrapper\">\n\t\t\t\t\t\t<span class=\"elementor-button-text\">D\u00e9couvrir nos formations en Data Science<\/span>\n\t\t<\/span>\n\t\t\t\t\t<\/a>\n\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t","protected":false},"excerpt":{"rendered":"<p>Les r\u00e9seaux LSTM sont une variation des r\u00e9seaux de neurones r\u00e9currents (RNN). Ils ont \u00e9t\u00e9 propos\u00e9s par J\u00fcrgen Schmidhuber et son \u00e9quipe au d\u00e9but des ann\u00e9es 1990. Alors que les RNN classiques ont des difficult\u00e9s \u00e0 traiter les d\u00e9pendances \u00e0 long terme en raison du probl\u00e8me du \u00ab\u00a0vanishing gradient\u00a0\u00bb, les LSTM ont \u00e9t\u00e9 sp\u00e9cialement con\u00e7us [&hellip;]<\/p>\n","protected":false},"author":45,"featured_media":215827,"comment_status":"open","ping_status":"open","sticky":false,"template":"elementor_theme","format":"standard","meta":{"content-type":"","inline_featured_image":false},"categories":[2497],"tags":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO Premium plugin v21.7 (Yoast SEO v21.7) - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Long Short Term Memory (LSTM) : de quoi s\u2019agit-il ?<\/title>\n<meta name=\"description\" content=\"D\u00e9couvrez tout ce qu&#039;il y a \u00e0 savoir \u00e0 propos du Long Short Term Memory ou LSTM, un type de r\u00e9seau de neurones sp\u00e9cialement con\u00e7u pour contourner le probl\u00e8me du vanishing gradient.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/datascientest.com\/long-short-term-memory-tout-savoir\" \/>\n<meta property=\"og:locale\" content=\"fr_FR\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Long Short Term Memory (LSTM) : de quoi s\u2019agit-il ?\" \/>\n<meta property=\"og:url\" content=\"https:\/\/datascientest.com\/long-short-term-memory-tout-savoir\" \/>\n<meta property=\"og:site_name\" content=\"Formation Data Science | DataScientest.com\" \/>\n<meta property=\"article:published_time\" content=\"2023-10-02T15:03:43+00:00\" \/>\n<meta property=\"article:modified_time\" content=\"2024-05-16T15:30:05+00:00\" \/>\n<meta property=\"og:image\" content=\"https:\/\/datascientest.com\/wp-content\/uploads\/2023\/10\/Long-Short-term-memory-LSTM.png\" \/>\n\t<meta property=\"og:image:width\" content=\"1200\" \/>\n\t<meta property=\"og:image:height\" content=\"600\" \/>\n\t<meta property=\"og:image:type\" content=\"image\/png\" \/>\n<meta name=\"author\" content=\"Daniel\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:label1\" content=\"\u00c9crit par\" \/>\n\t<meta name=\"twitter:data1\" content=\"Daniel\" \/>\n\t<meta name=\"twitter:label2\" content=\"Dur\u00e9e de lecture estim\u00e9e\" \/>\n\t<meta name=\"twitter:data2\" content=\"7 minutes\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\/\/schema.org\",\"@graph\":[{\"@type\":\"WebPage\",\"@id\":\"https:\/\/datascientest.com\/long-short-term-memory-tout-savoir\",\"url\":\"https:\/\/datascientest.com\/long-short-term-memory-tout-savoir\",\"name\":\"Long Short Term Memory (LSTM) : de quoi s\u2019agit-il ?\",\"isPartOf\":{\"@id\":\"https:\/\/datascientest.com\/#website\"},\"datePublished\":\"2023-10-02T15:03:43+00:00\",\"dateModified\":\"2024-05-16T15:30:05+00:00\",\"author\":{\"@id\":\"https:\/\/datascientest.com\/#\/schema\/person\/5a86e30c5090234fb43dd8baa83c44b0\"},\"description\":\"D\u00e9couvrez tout ce qu'il y a \u00e0 savoir \u00e0 propos du Long Short Term Memory ou LSTM, un type de r\u00e9seau de neurones sp\u00e9cialement con\u00e7u pour contourner le probl\u00e8me du vanishing gradient.\",\"inLanguage\":\"fr-FR\",\"potentialAction\":[{\"@type\":\"ReadAction\",\"target\":[\"https:\/\/datascientest.com\/long-short-term-memory-tout-savoir\"]}]},{\"@type\":\"WebSite\",\"@id\":\"https:\/\/datascientest.com\/#website\",\"url\":\"https:\/\/datascientest.com\/\",\"name\":\"Formation Data Science | DataScientest.com\",\"description\":\"Leader de la formation en Data Science\",\"potentialAction\":[{\"@type\":\"SearchAction\",\"target\":{\"@type\":\"EntryPoint\",\"urlTemplate\":\"https:\/\/datascientest.com\/?s={search_term_string}\"},\"query-input\":\"required name=search_term_string\"}],\"inLanguage\":\"fr-FR\"},{\"@type\":\"Person\",\"@id\":\"https:\/\/datascientest.com\/#\/schema\/person\/5a86e30c5090234fb43dd8baa83c44b0\",\"name\":\"Daniel\",\"image\":{\"@type\":\"ImageObject\",\"inLanguage\":\"fr-FR\",\"@id\":\"https:\/\/datascientest.com\/#\/schema\/person\/image\/\",\"url\":\"https:\/\/datascientest.com\/wp-content\/uploads\/2023\/10\/dst.svg\",\"contentUrl\":\"https:\/\/datascientest.com\/wp-content\/uploads\/2023\/10\/dst.svg\",\"caption\":\"Daniel\"},\"sameAs\":[\"https:\/\/www.linkedin.com\/school\/datascientest\/\",\"user\"],\"jobTitle\":\"ing\u00e9nieur logiciel\",\"worksFor\":\"DataScientest\",\"url\":\"https:\/\/datascientest.com\/author\/team-redac\"}]}<\/script>\n<!-- \/ Yoast SEO Premium plugin. -->","yoast_head_json":{"title":"Long Short Term Memory (LSTM) : de quoi s\u2019agit-il ?","description":"D\u00e9couvrez tout ce qu'il y a \u00e0 savoir \u00e0 propos du Long Short Term Memory ou LSTM, un type de r\u00e9seau de neurones sp\u00e9cialement con\u00e7u pour contourner le probl\u00e8me du vanishing gradient.","robots":{"index":"index","follow":"follow","max-snippet":"max-snippet:-1","max-image-preview":"max-image-preview:large","max-video-preview":"max-video-preview:-1"},"canonical":"https:\/\/datascientest.com\/long-short-term-memory-tout-savoir","og_locale":"fr_FR","og_type":"article","og_title":"Long Short Term Memory (LSTM) : de quoi s\u2019agit-il ?","og_url":"https:\/\/datascientest.com\/long-short-term-memory-tout-savoir","og_site_name":"Formation Data Science | DataScientest.com","article_published_time":"2023-10-02T15:03:43+00:00","article_modified_time":"2024-05-16T15:30:05+00:00","og_image":[{"width":1200,"height":600,"url":"https:\/\/datascientest.com\/wp-content\/uploads\/2023\/10\/Long-Short-term-memory-LSTM.png","type":"image\/png"}],"author":"Daniel","twitter_card":"summary_large_image","twitter_misc":{"\u00c9crit par":"Daniel","Dur\u00e9e de lecture estim\u00e9e":"7 minutes"},"schema":{"@context":"https:\/\/schema.org","@graph":[{"@type":"WebPage","@id":"https:\/\/datascientest.com\/long-short-term-memory-tout-savoir","url":"https:\/\/datascientest.com\/long-short-term-memory-tout-savoir","name":"Long Short Term Memory (LSTM) : de quoi s\u2019agit-il ?","isPartOf":{"@id":"https:\/\/datascientest.com\/#website"},"datePublished":"2023-10-02T15:03:43+00:00","dateModified":"2024-05-16T15:30:05+00:00","author":{"@id":"https:\/\/datascientest.com\/#\/schema\/person\/5a86e30c5090234fb43dd8baa83c44b0"},"description":"D\u00e9couvrez tout ce qu'il y a \u00e0 savoir \u00e0 propos du Long Short Term Memory ou LSTM, un type de r\u00e9seau de neurones sp\u00e9cialement con\u00e7u pour contourner le probl\u00e8me du vanishing gradient.","inLanguage":"fr-FR","potentialAction":[{"@type":"ReadAction","target":["https:\/\/datascientest.com\/long-short-term-memory-tout-savoir"]}]},{"@type":"WebSite","@id":"https:\/\/datascientest.com\/#website","url":"https:\/\/datascientest.com\/","name":"Formation Data Science | DataScientest.com","description":"Leader de la formation en Data Science","potentialAction":[{"@type":"SearchAction","target":{"@type":"EntryPoint","urlTemplate":"https:\/\/datascientest.com\/?s={search_term_string}"},"query-input":"required name=search_term_string"}],"inLanguage":"fr-FR"},{"@type":"Person","@id":"https:\/\/datascientest.com\/#\/schema\/person\/5a86e30c5090234fb43dd8baa83c44b0","name":"Daniel","image":{"@type":"ImageObject","inLanguage":"fr-FR","@id":"https:\/\/datascientest.com\/#\/schema\/person\/image\/","url":"https:\/\/datascientest.com\/wp-content\/uploads\/2023\/10\/dst.svg","contentUrl":"https:\/\/datascientest.com\/wp-content\/uploads\/2023\/10\/dst.svg","caption":"Daniel"},"sameAs":["https:\/\/www.linkedin.com\/school\/datascientest\/","user"],"jobTitle":"ing\u00e9nieur logiciel","worksFor":"DataScientest","url":"https:\/\/datascientest.com\/author\/team-redac"}]}},"_links":{"self":[{"href":"https:\/\/datascientest.com\/wp-json\/wp\/v2\/posts\/215825"}],"collection":[{"href":"https:\/\/datascientest.com\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/datascientest.com\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/datascientest.com\/wp-json\/wp\/v2\/users\/45"}],"replies":[{"embeddable":true,"href":"https:\/\/datascientest.com\/wp-json\/wp\/v2\/comments?post=215825"}],"version-history":[{"count":0,"href":"https:\/\/datascientest.com\/wp-json\/wp\/v2\/posts\/215825\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/datascientest.com\/wp-json\/wp\/v2\/media\/215827"}],"wp:attachment":[{"href":"https:\/\/datascientest.com\/wp-json\/wp\/v2\/media?parent=215825"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/datascientest.com\/wp-json\/wp\/v2\/categories?post=215825"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/datascientest.com\/wp-json\/wp\/v2\/tags?post=215825"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}