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18.945 26.9117 18.9111 27.205 18.5726C27.4984 18.2341 27.4645 17.7264 27.126 17.433L21.9695 12.818L27.7466 6.49927Z\" fill=\"#249EDC\"/\u003E\r\n\u003C/svg\u003E\u003C/span\u003E\r\n\u003C/div\u003E","cssContent":".share-icon__svg *{pointer-events:none}.share-icon-group{display:flex;gap:24px;align-items:center}.share-icon{display:inline-block}.share-icon svg{height:32px}.share-icon__svg:hover path{fill:var(--ui-02);transition:300ms ease fill}.share-icon__svg path{transition:300ms ease fill}","jsContent":"const pageURL = window.location.href;\r\n\r\ndocument.addEventListener('click', (e) =\u003E {\r\n\r\nconst targetClassesArr = [...e.target.classList];\r\nconsole.log(targetClassesArr);\r\nif (targetClassesArr.includes('share-icon--linkedin')) {\r\nconst shareURL = `https://www.linkedin.com/sharing/share-offsite/?url=${pageURL}`;\r\nwindow.open(shareURL, '_blank');\r\n}\r\nif (targetClassesArr.includes('share-icon--facebook')) {\r\nconst shareURL = `http://www.facebook.com/sharer/sharer.php?u=${pageURL}`;\r\nwindow.open(shareURL, '_blank');\r\n}\r\nif (targetClassesArr.includes('share-icon--twitter')) {\r\nconst shareURL = `https://twitter.com/intent/tweet?url=${pageURL}`;\r\nwindow.open(shareURL, '_blank');\r\n}\r\nif (targetClassesArr.includes('share-icon--email')) {\r\nconst shareURL = `mailto:?subject=Check out this news from Snowflake&body=${pageURL}`;\r\nwindow.open(shareURL);\r\n}\r\n\r\n\r\n\r\n\r\n\r\n});","isGSAPEnabled":false,":type":"snowflake-site/components/markup-editor"}},":itemsOrder":["markup_editor"],"appliedCssClassNames":"snowflake-responsive-container-inner-padding-small"}},":itemsOrder":["container_949147658"]},"cq:LiveSyncConfig":{"cq:isDeep":true,"cq:rolloutConfigs":[],"cq:master":"/content/experience-fragments/snowflake-site/language-masters/fr/site/share-icons/share-icons",":type":"cq:LiveCopy"}},":itemsOrder":["root","cq:LiveSyncConfig"],"classNames":"aem-xf","appliedCssClassNames":"snowflake-responsive-component-top-padding-extra-small"}},":itemsOrder":["text","experiencefragment"],"appliedCssClassNames":"snowflake-responsive-container-inner-padding-small"},"flexible_column_content_container_2":{"layout":"SIMPLE","id":"fundamentals-main-content",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container",":items":{"container":{"columnClassNames":{"title_v2":"aem-GridColumn aem-GridColumn--default--12","text":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","layout":"RESPONSIVE_GRID","columnCount":12,"id":"overview",":type":"snowflake-site/components/container",":items":{"title_v2":{"id":"title-v2-37f0b192e6","type":"heading2","lines":["Présentation"],":type":"snowflake-site/components/title-v2"},"text":{"id":"text-16c8f50db7","text":"\u003Cp\u003ELe deep learning est un sous-domaine du machine learning qui exploite la puissance de réseaux neuronaux artificiels pour découvrir et modéliser automatiquement les schémas complexes cachés dans des données brutes. Il est devenu le moteur des systèmes d’\u003Ca href=\"https://www.snowflake.com/en/fundamentals/ai-programming-languages/\"\u003EIA modernes\u003C/a\u003E et a permis des avancées majeures en matière de reconnaissance d’images et de traitement du langage naturel. C’est également grâce au deep learning que les chatbots d’IA peuvent générer du texte qui ressemble à s’y méprendre au discours humain. Le deep learning sert également de base à des technologies autonomes telles que les véhicules sans conducteur et les robots intelligents, qui traitent des flux de capteurs en temps réel pour percevoir le monde et prendre des décisions en une fraction de seconde.\u003C/p\u003E\n\u003Cp\u003ECe guide explique en quoi consiste le deep learning, pourquoi il est important, ainsi que ses avantages et ses limites.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"}},":itemsOrder":["title_v2","text"],"appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small"},"container_copy":{"columnClassNames":{"title_v2":"aem-GridColumn aem-GridColumn--default--12","text":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","layout":"RESPONSIVE_GRID","columnCount":12,"id":"is",":type":"snowflake-site/components/container",":items":{"title_v2":{"id":"title-v2-4f0b60b636","additionalClasses":"headline-decoration","type":"heading2","lines":["Définition du deep learning"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-none"},"text":{"id":"text-83aafd1f2a","additionalClasses":"list--blue-bullets","text":"\u003Cp\u003ELe deep learning est un type avancé de \u003Ca href=\"https://www.snowflake.com/en/fundamentals/machine-learning-frameworks/\"\u003Emachine learning\u003C/a\u003E qui utilise des réseaux neuronaux multicouches pour apprendre automatiquement des schémas complexes directement à partir de \u003Ca href=\"https://www.snowflake.com/en/fundamentals/building-effective-machine-learning-pipelines/\"\u003Edonnées brutes\u003C/a\u003E. Contrairement aux algorithmes traditionnels de machine learning, le deep learning n’a pas besoin qu’un humain lui indique à quelles features s’intéresser, comme les bords et les couleurs d’une image ou les associations de mots courantes dans un texte. À la place, il s’appuie sur des réseaux avec de nombreuses couches de neurones artificiels qui déterminent automatiquement quelles sont les features importantes. Ce processus d’auto-apprentissage nécessite des jeux de données d’entraînement beaucoup plus volumineux pour s’assurer que le \u003Ca href=\"https://www.snowflake.com/en/fundamentals/ml-models/\"\u003Emodèle\u003C/a\u003E comprend véritablement les schémas dans les données et ne se contente pas de les mémoriser. De plus, comme la plupart des réseaux neuronaux s’appuient sur des dizaines de couches de calcul différentes (qui effectuent toutes leurs opérations simultanément), le deep learning nécessite également une puissance de calcul nettement plus importante que les algorithmes traditionnels de machine learning.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"}},":itemsOrder":["title_v2","text"],"appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small"},"container_copy_2060034519":{"columnClassNames":{"title_v2_copy":"aem-GridColumn aem-GridColumn--default--12","text_copy":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","layout":"RESPONSIVE_GRID","columnCount":12,"id":"work",":type":"snowflake-site/components/container",":items":{"title_v2_copy":{"id":"title-v2-898b7a73ee","additionalClasses":"headline-decoration","type":"heading2","lines":["Importance du deep learning"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-none"},"text_copy":{"id":"text-0c984afa0d","text":"\u003Cp\u003ELe deep learning est capable d’extraire automatiquement des schémas significatifs à partir de données non structurées. Ainsi, il permet aux entreprises d’automatiser des tâches auparavant impossibles ou fastidieuses à réaliser, comme la détection des fraudes en temps réel, l’analyse d’images médicales ou encore la robotique en entrepôt. Les entreprises qui maîtrisent le deep learning acquièrent la capacité de traiter des données jusque-là inexploitées, d’automatiser des flux de travail complexes et d’identifier des opportunités commerciales plus rapidement que leurs concurrents. Par conséquent, c’est un outil essentiel pour soutenir le positionnement stratégique à long terme des entreprises dans une économie de plus en plus axée sur les données.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05 snowflake-responsive-component-bottom-padding-small"}},":itemsOrder":["title_v2_copy","text_copy"],"appliedCssClassNames":"snowflake-responsive-container-inner-padding-small"},"container_copy_copy_":{"columnClassNames":{"container":"aem-GridColumn aem-GridColumn--default--12","container_copy_copy__373061683":"aem-GridColumn aem-GridColumn--default--12","container_copy_copy__1985496925":"aem-GridColumn aem-GridColumn--default--12","container_copy_copy_":"aem-GridColumn aem-GridColumn--default--12","container_copy_copy":"aem-GridColumn aem-GridColumn--default--12","container_copy_copy_259718203":"aem-GridColumn aem-GridColumn--default--12","container_copy_289473020":"aem-GridColumn aem-GridColumn--default--12","container_copy":"aem-GridColumn aem-GridColumn--default--12","container_copy_copy__1190438074":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","layout":"RESPONSIVE_GRID","columnCount":12,"id":"rel",":type":"snowflake-site/components/container",":items":{"container_copy_copy":{"columnClassNames":{"title_v2":"aem-GridColumn aem-GridColumn--default--12","text":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","layout":"RESPONSIVE_GRID","columnCount":12,"id":"use",":type":"snowflake-site/components/container",":items":{"title_v2":{"id":"title-v2-2772eb2850","additionalClasses":"headline-decoration","type":"heading2","lines":["Exemples et cas d’usage du deep learning"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-none"},"text":{"id":"text-cf5c8cb049","text":"\u003Cp\u003EDes modèles de deep learning sont déjà à l’œuvre dans de nombreux secteurs. Voici quelques exemples :\u003C/p\u003E\n\u003Cp\u003E \u003C/p\u003E\n\u003Ch3\u003EDétection des fraudes dans le secteur financier\u003C/h3\u003E\n\u003Cp\u003EDes systèmes de deep learning analysent en temps réel les tendances autour des transactions pour identifier les activités suspectes qui s’écartent du comportement type des clients. Ces modèles peuvent signaler des transactions à haut risque pour qu’elles soient examinées ou les bloquer automatiquement, ce qui peut contribuer à réduire les pertes liées aux fraudes et à éviter aux clients des débits non autorisés. \u003C/p\u003E\n\u003Cp\u003E \u003C/p\u003E\n\u003Ch3\u003EMaintenance prédictive dans le secteur de l’industrie\u003C/h3\u003E\n\u003Cp\u003ELe deep learning analyse des données de capteurs sur des machines industrielles (vibrations, température, signaux acoustiques, etc.), afin d’identifier les signes précurseurs d’une défaillance matérielle imminente. Cette capacité prédictive permet aux fabricants de prévoir la maintenance pendant les temps d’arrêt planifiés, ce qui réduit considérablement les interruptions coûteuses et prolonge la durée de vie du matériel, tout en optimisant les coûts de maintenance.\u003C/p\u003E\n\u003Cp\u003E \u003C/p\u003E\n\u003Ch3\u003ERecommandations personnalisées dans le retail\u003C/h3\u003E\n\u003Cp\u003EDes plateformes de e-commerce utilisent le deep learning pour analyser l’historique de navigation d’un client, ses habitudes d’achat et sa similarité avec d’autres clients, afin de pouvoir lui recommander d’autres produits susceptibles de l’intéresser. En montrant aux acheteurs des suggestions plus personnalisées, le deep learning peut favoriser l’engagement des clients et améliorer les taux de conversion, en fonction de la mise en œuvre et du contexte. \u003C/p\u003E\n\u003Cp\u003E \u003C/p\u003E\n\u003Ch3\u003EImagerie et diagnostic médicaux\u003C/h3\u003E\n\u003Cp\u003EDes modèles de deep learning entraînés sur des millions d’images médicales (radiographies, scanners, IRM, rétinographies, etc.) peuvent détecter des maladies comme des cancers, des maladies cardiaques ou encore des troubles oculaires. Cette technologie permet d’accélérer le diagnostic, de réduire les erreurs humaines et de répondre à la pénurie mondiale de spécialistes dans les déserts médicaux. Dans certaines tâches et études étroitement définies, des modèles de deep learning ont démontré des performances comparables à celles de cliniciens. Toutefois, leur efficacité réelle dépend de la validation, de l’intégration des flux de travail et de la supervision clinique. \u003C/p\u003E\n\u003Cp\u003E \u003C/p\u003E\n\u003Ch3\u003ETraitement du langage naturel et chatbots\u003C/h3\u003E\n\u003Cp\u003ELe deep learning alimente des systèmes d’IA conversationnels qui comprennent le langage humain, ce qui permet à des chatbots de fournir une assistance client, de répondre à des questions et de réaliser des transactions sans intervention humaine. Grâce à un apprentissage basé sur d’importants volumes de texte et de données conversationnelles, ces bots sont capables de traiter des demandes de plus en plus complexes et de fournir des réponses naturelles et utiles.\u003C/p\u003E\n\u003Cp\u003E \u003C/p\u003E\n\u003Ch3\u003EVéhicules et robots autonomes\u003C/h3\u003E\n\u003Cp\u003ELes voitures et robots autonomes s’appuient sur le deep learning pour traiter des flux de caméras, de données lidar et de capteurs. Cela leur permet de comprendre leur environnement, de détecter les obstacles et de prendre des décisions de navigation en temps réel. Ainsi capables de percevoir le monde qui les entoure, ces systèmes autonomes peuvent s’adapter aux variations de l’état des routes, de la météo et du comportement humain.\u003C/p\u003E\n\u003Cp\u003E \u003C/p\u003E\n\u003Ch3\u003EReconnaissance vocale et traitement audio\u003C/h3\u003E\n\u003Cp\u003EDes modèles de deep learning convertissent des discours oraux en texte avec une précision remarquable. Ils permettent ainsi le fonctionnement d’assistants vocaux comme Siri et Alexa, ainsi que d’outils d’accessibilité pour les personnes malentendantes. Ces systèmes apprennent à gérer différents accents, les bruits de fond ainsi que des schémas dans les discours, de façon à permettre des interactions vocales pratiques avec un large éventail d’appareils et de services.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05 snowflake-responsive-component-bottom-padding-small"}},":itemsOrder":["title_v2","text"],"appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small"},"container_copy_copy_":{"columnClassNames":{"title_v2":"aem-GridColumn aem-GridColumn--default--12","text":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","layout":"RESPONSIVE_GRID","columnCount":12,"id":"and",":type":"snowflake-site/components/container",":items":{"title_v2":{"id":"title-v2-373d4edf03","additionalClasses":"headline-decoration","type":"heading2","lines":["Fonctionnement du deep learning"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-none"},"text":{"id":"text-4a709a1664","text":"\u003Cp\u003ELes modèles de deep learning sont créés à l’aide de réseaux complexes composés de milliers de neurones artificiels. Ces réseaux reposent sur des opérations mathématiques qui apprennent automatiquement des schémas à partir d’exemples étiquetés, en ajustant des millions de paramètres internes par essais et erreurs jusqu’à ce que ces modèles soient capables de prédire ou de reconnaître avec précision de nouvelles données qu’ils n’avaient jamais vues auparavant.\u003C/p\u003E\n\u003Cp\u003EChaque réseau est composé de trois éléments fondamentaux : une couche d’entrée où des données étiquetées sont ingérées ; plusieurs couches cachées de neurones qui analysent les données et s’affinent à chaque passage ; et une couche de sortie où la prédiction finale est formulée. \u003C/p\u003E\n\u003Cp\u003ESupposons que vous voulez entraîner un \u003Ca href=\"https://www.snowflake.com/fr/fundamentals/neural-network/\"\u003Eréseau neuronal\u003C/a\u003E à faire la différence entre des photos de chats et de chiens. Vous commencez par l’alimenter avec des milliers d’images étiquetées « chien » ou « chat », afin qu’il puisse identifier par lui-même les différences entre elles.\u003C/p\u003E\n\u003Cp\u003ELa première couche cachée peut apprendre à détecter des schémas simples, comme les bords et les coins. La deuxième couche cachée combine ces bords en formes, comme des cercles et des lignes. Une troisième couche peut reconnaître des composants comme des « oreilles pointues », une « truffe humide », etc. À chaque couche, le réseau développe une compréhension plus sophistiquée, passant des pixels bruts à des concepts significatifs.\u003C/p\u003E\n\u003Cp\u003ELa dernière couche contient la prédiction du réseau : un score de probabilité indiquant s’il pense que l’image montre un chien ou un chat. Si le réseau se trompe (c.-à-d., si la prédiction ne correspond pas à l’étiquette des données d’origine), il réessaye automatiquement, en accordant plus ou moins d’importance aux différentes features de l’image. Il répète ensuite ce processus jusqu’à être capable de distinguer correctement un chien et un chat avec une grande précision sur les données de test retenues, en fonction de la qualité et de la diversité des données d’entraînement et de la conception du modèle. \u003C/p\u003E\n\u003Cp\u003EUn réseau neuronal apprend de ses erreurs selon un processus appelé rétropropagation, qui consiste à reculer à travers les couches jusqu’à trouver les features qui ont le plus contribué à l’erreur dans la prédiction. Une formule mathématique connue sous le nom de fonction de perte lui indique alors la marge d’erreur à corriger en cas de problème. Si un modèle se trompe largement, par exemple en prédisant avec une confiance de 95 % qu’un chat est en fait un chien sur une image, il examinera les features qui l’ont poussé à faire une mauvaise prédiction, afin d’augmenter ou de diminuer l’importance qu’il leur accorde. Si le modèle se trompe moins lourdement (s’il prédit avec seulement 51 % de confiance qu’il s’agit d’une photo d’un chien), les ajustements seront bien plus légers.\u003C/p\u003E\n\u003Cp\u003EC’est là que réside toute la puissance du deep learning : une fois ce processus d’entraînement configuré, il découvre automatiquement les features et les représentations utiles, sans que l’utilisateur n’ait à les définir manuellement. Le réseau apprend ce qui compte. Et plus vous lui fournissez de données et de puissance de calcul, plus il peut apprendre des schémas complexes et repousser les limites de ce que l’intelligence artificielle peut accomplir.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05 snowflake-responsive-component-bottom-padding-small"}},":itemsOrder":["title_v2","text"],"appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small"},"container_copy_copy_259718203":{"columnClassNames":{"title_v2":"aem-GridColumn aem-GridColumn--default--12","text":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","layout":"RESPONSIVE_GRID","columnCount":12,"id":"how",":type":"snowflake-site/components/container",":items":{"title_v2":{"id":"title-v2-8b14dc1a3e","additionalClasses":"headline-decoration","type":"heading2","lines":["Types de modèles de deep learning"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-none"},"text":{"id":"text-1520be820b","text":"\u003Cp\u003EIl existe une petite dizaine d’architectures de deep learning différentes, chacune conçue pour des types de données et des tâches spécifiques. Voici les principales.\u003C/p\u003E\r\n\u003Cp\u003E \u003C/p\u003E\r\n\u003Ch3\u003ERéseaux neuronaux convolutifs (Convolutional Neural Networks, CNN)\u003C/h3\u003E\r\n\u003Cp\u003ELes CNN sont spécifiquement conçus pour traiter des données en grille (comme des images) en recherchant des schémas tels que des bords, des textures et des formes. Comme les CNN comprennent les relations entre les pixels à proximité les uns des autres, ils excellent dans des tâches de vision par ordinateur : classification d’images, détection d’objets, reconnaissance faciale, analyse d’images médicales, etc. Ils sont ainsi très efficaces pour créer différentes technologies, des applications photo sur smartphone qui identifient les visages aux véhicules autonomes qui détectent les piétons et les panneaux de signalisation.\u003C/p\u003E\r\n\u003Cp\u003E \u003C/p\u003E\r\n\u003Ch3\u003ERéseaux neuronaux récurrents (Recurrent Neural Networks, RNN)\u003C/h3\u003E\r\n\u003Cp\u003ELes RNN sont conçus pour des tâches dans lesquelles il est important de maintenir l’ordre d’apparition des données, comme l’analyse de phrases dans un document ou d’images dans une vidéo. Comme ils sont capables de traiter de nouvelles données tout en se souvenant des données qu’ils viennent d’analyser, les RNN sont utiles pour la traduction linguistique, la reconnaissance vocale et la \u003Ca href=\"https://www.snowflake.com/fr/fundamentals/time-series-analysis/\"\u003Eprédiction de séries temporelles\u003C/a\u003E. Les réseaux transformeurs, plus récents, les ont largement remplacés pour de nombreuses tâches linguistiques, mais les RNN restent précieux pour prendre en charge des flux continus de données (tels que des relevés de capteurs en temps réel), ou lorsque les ressources de calcul sont limitées.\u003C/p\u003E\r\n\u003Cp\u003E \u003C/p\u003E\r\n\u003Ch3\u003ERéseaux antagonistes génératifs (Generative Adversarial Networks, GAN)\u003C/h3\u003E\r\n\u003Cp\u003ELes GAN se composent de deux réseaux neuronaux en concurrence : un générateur qui crée des \u003Ca href=\"https://www.snowflake.com/fr/fundamentals/synthetic-data/\"\u003Edonnées synthétiques\u003C/a\u003E (comme de fausses images) et un discriminateur qui tente de distinguer les données réelles des fausses. Grâce à ce processus d’entraînement antagoniste, le générateur devient de plus en plus habile à produire des résultats réalistes. De ce fait, les GAN parviennent à créer des images photoréalistes, à générer des données d’entraînement synthétiques et même à produire des deepfakes. Ils sont utilisés pour créer des œuvres d’art, améliorer des images en basse résolution, générer des visages réalistes de personnes qui n’existent pas et aider à concevoir de nouvelles molécules pour la découverte de médicaments.\u003C/p\u003E\r\n\u003Cp\u003E \u003C/p\u003E\r\n\u003Ch3\u003ERéseaux transformeurs\u003C/h3\u003E\r\n\u003Cp\u003ELes transformeurs ont révolutionné le traitement du langage naturel en utilisant un « mécanisme d’attention » qui permet au réseau de se concentrer simultanément sur les données d’entrée les plus pertinentes, plutôt que de les traiter de manière séquentielle. Cette architecture sous-tend les \u003Ca href=\"https://www.snowflake.com/fr/fundamentals/large-language-model/\"\u003Egrands modèles de langage\u003C/a\u003E modernes tels que GPT et Claude et leur permet de comprendre le contexte sur de longs passages de texte, d’écrire comme un humain et d’effectuer certaines tâches (telles que la traduction et la synthèse) avec une précision sans précédent. Les transformeurs ont également prouvé leur efficacité au-delà de la linguistique : ainsi, grâce à des adaptations récentes, ils se révèlent remarquablement performants pour la vision par ordinateur et même pour prédire la structure de protéines.\u003C/p\u003E\r\n\u003Cp\u003E \u003C/p\u003E\r\n\u003Ch3\u003EAuto‑encodeurs\u003C/h3\u003E\r\n\u003Cp\u003ELes auto-encodeurs fonctionnent en compressant les données jusqu’à leurs features les plus essentielles, puis en les reconstituant à partir de cette forme compressée. Ainsi, ils sont utiles pour repérer des schémas inhabituels (tout ce qui ne peut pas être reconstruit correctement est probablement anormal), nettoyer les données bruyantes et ramener à l’essentiel les jeux de données complexes. Comme ils sont capables de repérer rapidement les anomalies dans des données, les auto-encodeurs sont utiles pour détecter des transactions bancaires frauduleuses ou repérer des défauts sur des produits sur les chaînes de montage.\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05 snowflake-responsive-component-bottom-padding-small"}},":itemsOrder":["title_v2","text"],"appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small"},"container_copy_289473020":{"columnClassNames":{"title_v2_copy":"aem-GridColumn aem-GridColumn--default--12","text_copy":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","layout":"RESPONSIVE_GRID","columnCount":12,"id":"equation",":type":"snowflake-site/components/container",":items":{"title_v2_copy":{"id":"title-v2-cc513590f6","additionalClasses":"headline-decoration","type":"heading2","lines":["Les principales différences entre le machine learning, le deep learning et l’IA générative"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-none"},"text_copy":{"id":"text-c772828fa8","text":"\u003Cp\u003EAujourd’hui, le développement de modèles d’IA repose sur trois paradigmes liés mais distincts. En voici les principales différences. \u003C/p\u003E\r\n\u003Cp\u003E \u003C/p\u003E\r\n\u003Ch3\u003EMachine learning\u003C/h3\u003E\r\n\u003Cp\u003ELes modèles de machine learning utilisent des algorithmes qui apprennent des schémas à partir de données, mais ils exigent généralement que des humains conçoivent et extraient manuellement les features pertinentes avant que l’algorithme ne puisse en tirer des enseignements. Ces systèmes fonctionnent bien avec des données tabulaires structurées et des jeux de données relativement modestes, ce qui les rend pratiques pour des applications telles que l’évaluation de scores de crédit, la segmentation client ou encore des systèmes de recommandation simples. Les modèles de machine learning sont généralement plus faciles à interpréter que les modèles de deep learning, et leur entraînement et leur déploiement nécessitent moins de puissance de calcul.\u003C/p\u003E\r\n\u003Cp\u003E \u003C/p\u003E\r\n\u003Ch3\u003EDeep learning\u003C/h3\u003E\r\n\u003Cp\u003ELe deep learning utilise des réseaux neuronaux multicouches qui découvrent automatiquement les features importantes. Ainsi, il ne nécessite pas de feature engineering manuel, contrairement au machine learning traditionnel. Les systèmes de deep learning excellent sur des données non structurées (images, audio, texte, etc.), mais nécessitent de grands jeux de données d’entraînement (souvent des millions d’exemples) et des ressources de calcul substantielles pour un apprentissage efficace. Le deep learning permet des applications qui nécessitent de comprendre des schémas complexes : reconnaissance faciale, véhicules autonomes, diagnostics sur des images médicales, systèmes de reconnaissance vocale…\u003C/p\u003E\r\n\u003Cp\u003E \u003C/p\u003E\r\n\u003Ch3\u003EIA générative\u003C/h3\u003E\r\n\u003Cp\u003EL’\u003Ca href=\"https://www.snowflake.com/en/fundamentals/generative-ai/\"\u003EIA générative\u003C/a\u003E est un sous-domaine du deep learning, mais plutôt que de classer des données ou de prédire des résultats à partir de données existantes, elle est spécifiquement conçue pour créer de nouveaux contenus, notamment du texte, des images, de la musique, du code ou encore des vidéos. L’entraînement de ces systèmes nécessite des jeux de données particulièrement massifs (souvent des milliards d’exemples), à l’aide d’architectures telles que des transformeurs et des GAN qui apprennent suffisamment bien les structures et les schémas sous-jacents des données d’entraînement pour générer des résultats inédits et réalistes. L’IA générative est à la base d’applications comme ChatGPT et Claude (IA conversationnelle), DALL-E et Midjourney (génération d’images), GitHub Copilot (complétion de code), ainsi que de systèmes qui créent des données d’entraînement synthétiques ou du contenu personnalisé à grande échelle.\u003C/p\u003E\r\n\u003Cp\u003EOutre ces trois paradigmes d’IA, il en existe quelques autres également notables. L’IA classique (ou symbolique) utilise des règles explicites, une logique et des connaissances programmées par des humains ; c’est le paradigme utilisé par les systèmes experts et les chatbots basés sur des règles. Selon le paradigme de l’apprentissage par renforcement, les \u003Ca href=\"https://www.snowflake.com/fr/fundamentals/what-are-ai-agents-understanding-their-role-and-impact/\"\u003Eagents d’IA\u003C/a\u003E interagissent avec leur environnement et reçoivent des récompenses ou des pénalités, selon les actions qu’ils entreprennent. Ce modèle est souvent déployé dans des systèmes de contrôle robotique et des moteurs de recommandation qui tirent des enseignements de l’engagement des utilisateurs. Inspirés de l’évolution biologique, les algorithmes évolutionnaires permettent à des modèles de s’améliorer continuellement et de s’ajuster au fil du temps ; ils sont utilisés pour résoudre des problèmes tels que la conception de réseaux neuronaux ou l’optimisation de la supply chain. L’IA neuro-symbolique combine des réseaux neuronaux (qui apprennent à partir de données) avec un raisonnement symbolique (des règles logiques et des connaissances). Ce paradigme émergent commence tout juste à faire son apparition dans des applications réelles pour améliorer les diagnostics médicaux et la cybersécurité.\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05 snowflake-responsive-component-bottom-padding-small"}},":itemsOrder":["title_v2_copy","text_copy"],"appliedCssClassNames":"snowflake-responsive-container-inner-padding-small"},"container_copy_copy__1985496925":{"columnClassNames":{"title_v2":"aem-GridColumn aem-GridColumn--default--12","text":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","layout":"RESPONSIVE_GRID","columnCount":12,"id":"why",":type":"snowflake-site/components/container",":items":{"title_v2":{"id":"title-v2-41d97c9e38","additionalClasses":"headline-decoration","type":"heading2","lines":["Avantages des modèles de deep learning"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-none"},"text":{"id":"text-bb9c016b0c","text":"\u003Cp\u003ELes algorithmes de deep learning présentent un certain nombre d’avantages par rapport à d’autres paradigmes d’IA. Voici quelques-uns de leurs principaux points forts.\u003C/p\u003E\n\u003Cp\u003E \u003C/p\u003E\n\u003Ch3\u003EIls réalisent des tâches complexes avec une grande précision\u003C/h3\u003E\n\u003Cp\u003ELe deep learning permet d’atteindre des performances de pointe sur certaines tâches complexes (p. ex., classification d’images et reconnaissance vocale), en fonction du modèle, des données et de la configuration d’évaluation. Ces modèles peuvent détecter dans les données des features et des relations subtiles qu’il serait presque impossible d’identifier ou de programmer explicitement pour un être humain, comme la reconnaissance des premiers signes de maladie dans des scans médicaux ou la prédiction des structures de protéines. Plus les tâches deviennent complexes, plus cette précision est avantageuse. C’est pourquoi le deep learning est l’approche privilégiée pour résoudre des problèmes dont on n’a pas pu venir à bout par le passé avec des méthodes traditionnelles.\u003C/p\u003E\n\u003Cp\u003E \u003C/p\u003E\n\u003Ch3\u003EIls identifient automatiquement les features pertinentes dans les données \u003C/h3\u003E\n\u003Cp\u003EContrairement au machine learning traditionnel, le deep learning permet de découvrir automatiquement les features importantes sans avoir à demander à des experts du domaine de les concevoir et de les extraire manuellement. Le réseau apprend lui-même les représentations hiérarchiques, en identifiant les bords dans les premières couches, en les combinant en formes dans les couches intermédiaires et en reconnaissant des concepts avancés dans les dernières couches. Cette automatisation réduit considérablement le temps de développement et permet au deep learning de résoudre des problèmes dans des domaines où même des experts humains pourraient avoir du mal à identifier les features pertinentes.\u003C/p\u003E\n\u003Cp\u003E \u003C/p\u003E\n\u003Ch3\u003EIls s’adaptent facilement à de grands jeux de données\u003C/h3\u003E\n\u003Cp\u003ELes modèles de deep learning s’améliorent de manière prévisible à mesure que vous leur fournissez davantage de données d’entraînement, alors que les algorithmes traditionnels de machine learning finissent généralement par atteindre un plafond. Grâce à cette évolutivité, les entreprises qui ont accès à des jeux de données massifs peuvent obtenir des performances nettement supérieures en investissant pour augmenter la collecte de données et la taille des modèles. À la faveur de cette relation entre le volume de données et les performances, les entreprises capables de collecter et de traiter des informations à grande échelle cumulent les avantages.\u003C/p\u003E\n\u003Cp\u003E \u003C/p\u003E\n\u003Ch3\u003EIls peuvent prendre des décisions en temps réel \u003C/h3\u003E\n\u003Cp\u003EUne fois entraînés, les modèles de deep learning peuvent traiter des informations et formuler des prédictions très rapidement. Ils permettent ainsi des applications en temps réel qui nécessitent des réponses instantanées. En raison de sa vitesse, le deep learning est adapté aux véhicules autonomes qui doivent détecter les obstacles et réagir immédiatement, aux systèmes de détection des fraudes qui évaluent les transactions au fur et à mesure et aux assistants vocaux qui répondent aux commandes vocales sans délai perceptible. Les optimisations du matériel moderne et les techniques de compression de modèles continuent d’améliorer la vitesse d’inférence, élargissant la gamme d’applications en temps réel.\u003C/p\u003E\n\u003Cp\u003E \u003C/p\u003E\n\u003Ch3\u003EIls excellent dans la gestion des données non structurées\u003C/h3\u003E\n\u003Cp\u003ELe deep learning excelle dans le traitement de types de données non structurées qui n’ont pas d’organisation tabulaire claire (images, vidéos, audio, textes, flux de capteurs, etc.), alors que les algorithmes traditionnels peinent à les prendre en charge. Cette capacité permet de tirer parti de l’énorme volume d’e-mails, d’enregistrements de service client, d’images de vidéosurveillance et de publications sur les réseaux sociaux que génèrent les entreprises. Grâce au deep learning, des données auparavant inutilisables peuvent désormais être analysées, ce qui permet de toutes nouvelles catégories d’applications et d’informations.\u003C/p\u003E\n\u003Cp\u003E \u003C/p\u003E\n\u003Ch3\u003EIls s’adaptent rapidement aux nouvelles tâches\u003C/h3\u003E\n\u003Cp\u003ELes modèles de deep learning entraînés sur une tâche peuvent souvent être adaptés à des tâches connexes avec un minimum d’entraînement supplémentaire, ce qui réduit considérablement les données et le temps nécessaires pour créer de nouvelles applications. Par exemple, un modèle entraîné à reconnaître des objets du quotidien peut être affiné pour identifier des conditions médicales spécifiques, en utilisant beaucoup moins d’images médicales qu’il n’en faudrait pour réaliser un entraînement à partir de zéro. Cette technique, connue sous le nom de transfer learning, permet aux entreprises d’exploiter des modèles existants comme points de départ, de façon à accélérer les cycles de développement et à rendre le deep learning plus accessible, même lorsqu’un domaine manque de données spécifiques.\u003C/p\u003E\n\u003Cp\u003E \u003C/p\u003E\n\u003Ch3\u003EIls apprennent toujours \u003C/h3\u003E\n\u003Cp\u003ELes systèmes de deep learning peuvent être continuellement mis à jour avec de nouvelles données, ce qui leur permet de s’adapter à l’évolution des schémas, de gagner en précision au fil du temps et de gérer des conditions changeantes sans réentraînement complet. Cette capacité d’apprentissage signifie que les modèles déployés en production peuvent s’améliorer à mesure qu’ils rencontrent des exemples plus concrets et s’adapter naturellement à l’évolution du comportement des utilisateurs, des conditions du marché ou des facteurs environnementaux. Comme ils s’améliorent ainsi progressivement, les systèmes de deep learning sont plus fiables et durables pour un déploiement à long terme que des systèmes statiques basés sur des règles.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05 snowflake-responsive-component-bottom-padding-small"}},":itemsOrder":["title_v2","text"],"appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small"},"container_copy":{"columnClassNames":{"title_v2_copy":"aem-GridColumn aem-GridColumn--default--12","text_copy":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","layout":"RESPONSIVE_GRID","columnCount":12,"id":"types",":type":"snowflake-site/components/container",":items":{"title_v2_copy":{"id":"title-v2-d8f4ec5bd3","additionalClasses":"headline-decoration","type":"heading2","lines":["Inconvénients des modèles de deep learning"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-none"},"text_copy":{"id":"text-43a7786f0a","text":"\u003Cp\u003EBien qu’ils soient extrêmement utiles dans un large éventail d’applications, les modèles de deep learning présentent également d’énormes défis en termes de coût, de consommation d’énergie, d’interprétabilité et de risque d’utilisation abusive. Voici les principaux inconvénients des modèles de deep learning.\u003C/p\u003E\n\u003Cp\u003E \u003C/p\u003E\n\u003Ch3\u003EIls ont besoin de beaucoup de puissance de calcul \u003C/h3\u003E\n\u003Cp\u003EL’entraînement de modèles de deep learning nécessite une puissance de calcul considérable, qui implique souvent du matériel spécialisé coûteux, comme des GPU fonctionnant pendant des jours voire des semaines. Leur consommation d’énergie peut être énorme : l’entraînement de grands modèles peut être énergivore, avec des exigences qui varient considérablement selon la taille du modèle, le matériel et la durée de l’entraînement. Le déploiement de modèles pour l’inférence en temps réel à grande échelle nécessite également des ressources de calcul permanentes et des investissements dans l’infrastructure. Par conséquent, d’un point de vue économique, le deep learning est tout simplement impossible pour certaines applications et petites entreprises.\u003C/p\u003E\n\u003Cp\u003E \u003C/p\u003E\n\u003Ch3\u003EIls nécessitent de grands jeux de données étiquetées\u003C/h3\u003E\n\u003Cp\u003ELes modèles de deep learning ont généralement besoin de milliers, voire de millions d’exemples d’entraînement étiquetés pour être performants, or la création de ces étiquettes nécessite souvent un effort humain et une expertise considérables. Dans des domaines spécialisés comme l’imagerie médicale ou le diagnostic de maladies rares, pour lesquels des experts doivent examiner et annoter manuellement chaque exemple, l’obtention de données étiquetées en quantité suffisante peut s’avérer extrêmement difficile ou coûteuse. À cause de ces besoins en données, le démarrage du deep learning s’avère compliqué, car il ne peut pas être appliqué efficacement sans d’abord investir massivement dans la collecte et l’étiquetage de données. Ses applications avancées sont donc hors de portée des entreprises qui ne disposent pas de ressources suffisantes en termes de données.\u003C/p\u003E\n\u003Cp\u003E \u003C/p\u003E\n\u003Ch3\u003EIls sont sensibles au surapprentissage\u003C/h3\u003E\n\u003Cp\u003ELes modèles de deep learning peuvent finir par mémoriser des données d’entraînement au lieu d’apprendre à identifier des schémas au sein de ces données. En cas de surapprentissage, un modèle est extrêmement performant sur des exemples d’entraînement, mais échoue lorsqu’il fait face à de nouvelles situations légèrement différentes, comme un système de reconnaissance faciale qui fonctionne parfaitement en laboratoire mais qui se retrouve en difficulté en production face à différentes conditions d’éclairage ou divers angles de caméra. La prévention du surapprentissage passe par différentes techniques (régularisation, abandon, tests de validation, etc.), mais même avec ces précautions, les modèles peuvent toujours apprendre des corrélations fausses qui ne se vérifient pas dans le monde réel.\u003C/p\u003E\n\u003Cp\u003E \u003C/p\u003E\n\u003Ch3\u003ELeurs opérations sont opaques \u003C/h3\u003E\n\u003Cp\u003EIl est souvent impossible de comprendre exactement pourquoi un modèle de deep learning a fait une prédiction particulière, ce qui s’avère problématique pour les applications qui requièrent des explications pour des raisons légales ou éthiques. Par exemple, un système d’approbation de prêt basé sur le deep learning peut rejeter un candidat sans être en mesure d’expliquer quels facteurs ont motivé cette décision, ce qui pourrait constituer une infraction aux lois sur les prêts équitables ou perpétuer des biais cachés. Ce problème de fonctionnement en « boîte noire » soulève des défis dans des secteurs réglementés comme la santé et les services financiers ; il est également difficile de déboguer les modèles en cas de défaillance ou de vérifier qu’ils prennent leurs décisions pour les bonnes raisons.\u003C/p\u003E\n\u003Cp\u003E \u003C/p\u003E\n\u003Ch3\u003EIls soulèvent des préoccupations éthiques majeures\u003C/h3\u003E\n\u003Cp\u003EÉtant donné que les modèles de deep learning tirent des enseignements de données historiques, ils absorbent et amplifient inévitablement les biais qui existent dans ces données, au risque de perpétuer des discriminations dans le recrutement, l’octroi de prêts, la justice pénale et d’autres domaines sensibles. Un système de reconnaissance faciale entraîné principalement sur des visages à la peau claire fonctionnera mal sur des personnes à la peau plus foncée, et un outil de présélection de CV entraîné sur des décisions d’embauche passées pourrait se révéler discriminatoire à l’égard des femmes ou des minorités. Au-delà des biais, le deep learning soulève un certain nombre de préoccupations éthiques concernant sa capacité à générer des deepfakes, son rôle dans la surveillance de masse et son utilisation avec des systèmes d’armes autonomes.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05 snowflake-responsive-component-bottom-padding-small"}},":itemsOrder":["title_v2_copy","text_copy"],"appliedCssClassNames":"snowflake-responsive-container-inner-padding-small"},"container":{"columnClassNames":{},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","layout":"RESPONSIVE_GRID","columnCount":12,"id":"advantages",":type":"snowflake-site/components/container",":items":{},":itemsOrder":[],"appliedCssClassNames":"snowflake-responsive-container-inner-padding-small"},"container_copy_copy__1190438074":{"columnClassNames":{"container_copy_2083166428":"aem-GridColumn aem-GridColumn--default--12","flexible_column_cont":"aem-GridColumn aem-GridColumn--default--12","container_copy_20831":"aem-GridColumn aem-GridColumn--default--12","container_copy":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","layout":"RESPONSIVE_GRID","columnCount":12,"id":"customer",":type":"snowflake-site/components/container",":items":{"container_copy":{"columnClassNames":{},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","layout":"RESPONSIVE_GRID","columnCount":12,"id":"limitation",":type":"snowflake-site/components/container",":items":{},":itemsOrder":[],"appliedCssClassNames":"snowflake-responsive-container-inner-padding-small"},"container_copy_2083166428":{"columnClassNames":{"title_v2_copy":"aem-GridColumn aem-GridColumn--default--12","text_copy":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","layout":"RESPONSIVE_GRID","columnCount":12,"id":"conslusion",":type":"snowflake-site/components/container",":items":{"title_v2_copy":{"id":"title-v2-ff17021be0","additionalClasses":"headline-decoration","type":"heading2","lines":["Conclusion"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-none"},"text_copy":{"id":"text-b2e8babe12","text":"\u003Cp\u003ELe deep learning a fondamentalement transformé l’intelligence artificielle en permettant à des machines d’apprendre automatiquement des schémas complexes à partir de données brutes. 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Cependant, si vous souhaitez créer et entraîner vous-même des modèles de deep learning, vous aurez besoin de compétences en programmation (typiquement Python) et d’au moins certaines bases en calcul, en algèbre linéaire et en statistiques pour travailler efficacement avec les frameworks et déboguer les problèmes.\u003C/p\u003E\n"},{"title":"Le deep learning est-il réellement utile pour résoudre des problèmes concrets ?","richText":"\u003Cp\u003ELe deep learning permet de résoudre des problèmes concrets qui étaient auparavant impossibles ou fastidieux à résoudre, en alimentant des systèmes variés, allant du diagnostic médical pour détecter des cancers aux véhicules autonomes. 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