{"id":86,"date":"2019-01-24T09:00:41","date_gmt":"2019-01-24T09:00:41","guid":{"rendered":"https:\/\/blog-github-com-preprod.go-vip.co\/fr\/?p=86"},"modified":"2019-01-24T09:00:41","modified_gmt":"2019-01-24T09:00:41","slug":"rapport-octoverse-sur-le-machine-learning","status":"publish","type":"post","link":"https:\/\/github.blog\/fr\/2019-01-24-rapport-octoverse-sur-le-machine-learning\/","title":{"rendered":"Rapport Octoverse sur le Machine Learning"},"content":{"rendered":"

Dans le rapport Octoverse 2018 de GitHub, il a \u00e9t\u00e9 constat\u00e9 que l\u2019apprentissage automatique (Machine Learning)<\/i> et la science des donn\u00e9es (Data Science<\/i>) \u00e9taient deux sujets tr\u00e8s pris\u00e9s sur GitHub. Ainsi, le projet tensorflow\/tensorflow b\u00e9n\u00e9ficie de l\u2019un des plus grands nombres de contributions, pytorch\/pytorch est l\u2019un des plus dynamiques, et Python se positionne sur la troisi\u00e8me marche du podium des langages les plus appr\u00e9ci\u00e9s sur GitHub. <\/span><\/p>\n

C\u2019est la raison pour laquelle GitHub se penche aujourd\u2019hui de plus pr\u00e8s sur l\u2019\u00e9tat du Machine Learning et la Data Science sur GitHub.<\/span><\/p>\n

GitHub a ainsi extrait des donn\u00e9es associ\u00e9es aux contributions effectu\u00e9es entre le 1<\/span>er<\/sup><\/span> janvier et le 31 d\u00e9cembre 2018, qu\u2019il s\u2019agisse de partager un morceau de code, d\u2019ouvrir ou de commenter une issue ou une Pull Request, ou encore de r\u00e9viser une Pull Request. GitHub a \u00e9galement utilis\u00e9 les donn\u00e9es du graphe de d\u00e9pendances pour les librairies les plus import\u00e9es. Ce graphe de d\u00e9pendances inclut la totalit\u00e9 des d\u00e9p\u00f4ts publics ainsi que les d\u00e9p\u00f4ts priv\u00e9s ayant choisi d\u2019activer cette fonctionnalit\u00e9. <\/span><\/p>\n

Langages de programmation<\/b><\/span><\/p>\n

\"Top<\/span><\/p>\n

GitHub a \u00e9tudi\u00e9 les contributeurs aux d\u00e9p\u00f4ts arborant l\u2019\u00e9tiquette Machine Learning<\/i>, et a class\u00e9 les principaux langages les plus utilis\u00e9s dans ces d\u00e9p\u00f4ts. Python pointe en t\u00eate parmi les r\u00e9f\u00e9rentiels li\u00e9s au Machine Learning, et arrive en troisi\u00e8me position au classement g\u00e9n\u00e9ral GitHub. A noter cependant que les activit\u00e9s li\u00e9es au Machine Learning ne sont pas toutes associ\u00e9es \u00e0 Python\u00a0: certains des langages les plus courants sur GitHub sont \u00e9galement utilis\u00e9s dans les projets de Machine Learning\u00a0: C++, JavaScript, Java, C#, Shell, et TypeScript figurent dans le Top 10 des langages de GitHub, ainsi que dans le Top 10 des projets du Machine Learning. On trouve les langages Julia, R et Scala dans le Top 10 des projets de Machine Learning, mais pas sur GitHub au sens large. Julia et R sont deux langages couramment utilis\u00e9s par les scientifiques des donn\u00e9es (data scientists<\/i>), et Scala est de plus en plus appr\u00e9ci\u00e9 pour interagir avec des syst\u00e8mes de Big Data comme Apache Spark.<\/span><\/p>\n

Principales librairies utilis\u00e9s pour le Machine Learning <\/i>et la Data Science <\/b><\/span><\/p>\n

\"Top<\/span><\/p>\n

GitHub a extrait des donn\u00e9es du graphe de d\u00e9pendances pour calculer le pourcentage de projets associ\u00e9s au Machine Learning ou \u00e0 la Data Science ayant import\u00e9 des librairies Python populaires. La liste ci-dessus indique les Top 10 librairies import\u00e9es par ces projets. <\/span><\/p>\n

Numpy<\/a><\/span>, une librairie qui prend en charge des op\u00e9rations math\u00e9matiques sur des donn\u00e9es multidimensionnelles, est la plus import\u00e9e\u00a0; elle est utilis\u00e9e dans pr\u00e8s des trois quarts des projets de Machine Learning et de Data Science. Scipy<\/span><\/a>, une librairie de calcul scientifique, pandas<\/span><\/a>, une librairie de gestion de jeux de donn\u00e9es, et matplotlib<\/span><\/a>, une biblioth\u00e8que de visualisations, sont utilis\u00e9es dans plus de 40\u00a0% des projets Machine Learning et de Data Science. Scikit-learn<\/span><\/a> est une librairie de Machine Learning populaire qui contient des impl\u00e9mentations d\u2019un grand nombre d\u2019algorithmes de Machine Learning\u00a0; elle est utilis\u00e9e dans pr\u00e8s de 40\u00a0% des projets. Pour sa part, tensorflow<\/span><\/a>, un outil con\u00e7u pour travailler avec des r\u00e9seaux neuronaux, est employ\u00e9 dans pr\u00e8s d\u2019un quart des librairies. Les autres figurant dans le Top\u00a010 sont des librairies utilitaires\u00a0: six<\/span><\/a> est une biblioth\u00e8que de compatibilit\u00e9 avec Python 2 et 3, tandis que python-dateutil<\/span><\/a> et pytz<\/span><\/a> sont con\u00e7us pour travailler avec des dates.<\/span><\/p>\n

Principaux projets d\u2019apprentissage automatique <\/b><\/span><\/p>\n

\"Top<\/span><\/p>\n

GitHub s\u2019est par ailleurs int\u00e9ress\u00e9 aux projets open source \u00e9tiquet\u00e9s \u00ab machine-learning \u00bb qui ont enregistr\u00e9 le plus grand nombre de contributions en 2018. Tensorflow<\/span><\/a> est de loin le projet le plus populaire, avec plus de cinq fois plus de contributeurs que son dauphin, scikit-learn<\/span><\/a>. Deux projets, explosion\/spaCy<\/span><\/a> et RasaHQ\/rasa_nlu<\/span><\/a>, sont consacr\u00e9s aux probl\u00e9matiques de traitement en langage naturel (NLP), et quatre autres, CMU-Perceptual-Computing-Lab\/openpose<\/span><\/a>, thtrieu\/darkflow<\/span><\/a>, ageitgey\/face_recognition<\/span><\/a> et tesseract-ocr\/tesseract<\/span><\/a>, sont d\u00e9di\u00e9s au traitement d\u2019images. Le code source<\/span><\/a> du langage Julia a \u00e9galement enregistr\u00e9 l\u2019un des plus grands nombres de contributions en 2018.<\/span><\/p>\n","protected":false},"excerpt":{"rendered":"

Dans le rapport Octoverse 2018 de GitHub, il a \u00e9t\u00e9 constat\u00e9 que l\u2019apprentissage automatique (Machine Learning) et la science des donn\u00e9es (Data Science) \u00e9taient deux sujets tr\u00e8s pris\u00e9s sur GitHub. Ainsi, le projet tensorflow\/tensorflow b\u00e9n\u00e9ficie de l\u2019un des plus grands nombres de contributions, pytorch\/pytorch est l\u2019un des plus dynamiques, et Python se positionne sur la troisi\u00e8me marche du podium des langages les plus appr\u00e9ci\u00e9s sur GitHub. <\/p>\n","protected":false},"author":1532,"featured_media":174,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_jetpack_memberships_contains_paid_content":false,"footnotes":""},"categories":[3,13],"tags":[],"coauthors":[],"class_list":["post-86","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-communaute","category-insights"],"yoast_head":"\nRapport Octoverse sur le Machine Learning - Le Blog GitHub<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/github.blog\/fr\/2019-01-24-rapport-octoverse-sur-le-machine-learning\/\" \/>\n<meta property=\"og:locale\" content=\"fr_FR\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Rapport Octoverse sur le Machine Learning\" \/>\n<meta property=\"og:description\" content=\"Dans le rapport Octoverse 2018 de GitHub, il a \u00e9t\u00e9 constat\u00e9 que l\u2019apprentissage automatique (Machine Learning) et la science des donn\u00e9es (Data Science) \u00e9taient deux sujets tr\u00e8s pris\u00e9s sur GitHub. Ainsi, le projet tensorflow\/tensorflow b\u00e9n\u00e9ficie de l\u2019un des plus grands nombres de contributions, pytorch\/pytorch est l\u2019un des plus dynamiques, et Python se positionne sur la troisi\u00e8me marche du podium des langages les plus appr\u00e9ci\u00e9s sur GitHub.\" \/>\n<meta property=\"og:url\" content=\"https:\/\/github.blog\/fr\/2019-01-24-rapport-octoverse-sur-le-machine-learning\/\" \/>\n<meta property=\"og:site_name\" content=\"Le Blog GitHub\" \/>\n<meta property=\"article:published_time\" content=\"2019-01-24T09:00:41+00:00\" \/>\n<meta property=\"og:image\" content=\"https:\/\/github.blog\/fr\/wp-content\/uploads\/sites\/4\/2019\/04\/machine-learning.png?fit=1608%2C832\" \/>\n\t<meta property=\"og:image:width\" content=\"1608\" \/>\n\t<meta property=\"og:image:height\" content=\"832\" \/>\n\t<meta property=\"og:image:type\" content=\"image\/png\" \/>\n<meta name=\"author\" content=\"Thomas Elliott\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:title\" content=\"Rapport Octoverse sur le Machine Learning\" \/>\n<meta name=\"twitter:description\" content=\"Dans le rapport Octoverse 2018 de GitHub, il a \u00e9t\u00e9 constat\u00e9 que l\u2019apprentissage automatique (Machine Learning) et la science des donn\u00e9es (Data Science) \u00e9taient deux sujets tr\u00e8s pris\u00e9s sur GitHub. 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