Bienvenue dans "Art and Science of Machine Learning". Ce cours se compose de 6 modules. Dans ce cours, nous allons voir les compétences essentielles que sont l'intuition, le bon sens et l'expérimentation, nécessaires pour ajuster vos modèles de ML et optimiser leurs performances. Nous verrons comment généraliser votre modèle à l'aide de techniques de régularisation, et nous évoquerons les effets des hyperparamètres tels que la taille de lot et le taux d'apprentissage sur les performances de votre modèle. Nous présenterons également certains des algorithmes d'optimisation les plus courants et vous montrerons comment spécifier une méthode d'optimisation dans votre code TensorFlow.
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关于 Machine Learning with TensorFlow on Google Cloud en Français 专项课程
Qu'est-ce que le machine learning et quels types de problèmes permet-il de résoudre ? Quelles sont les cinq phases permettant de traiter un cas d'utilisation à l'aide du machine learning, et pourquoi chaque étape est-elle essentielle ? Pourquoi les réseaux de neurones sont-ils désormais si courants ? Comment définir un problème d'apprentissage supervisé et trouver une solution adaptée et généralisable à l'aide de la descente de gradient et d'une méthode pertinente de création d'ensembles de données ? Apprenez à créer des modèles de machine learning distribués qui pourront évoluer dans TensorFlow, à adapter l'entraînement de ces modèles pour bénéficier d'une évolutivité horizontale et à obtenir des prédictions très performantes. Convertissez les données brutes en caractéristiques de sorte que les processus de ML soient en mesure d'identifier les propriétés importantes dans les données et générez des insights qui ont du sens en rapport avec la problématique. Enfin, découvrez comment intégrer à la fois la combinaison de paramètres permettant d'obtenir des modèles précis et généralisés, et une connaissance de la théorie indispensable pour résoudre des types spécifiques de problèmes de ML.
Vous expérimenterez le ML de bout en bout en commençant par créer une stratégie centrée sur le ML, puis en progressant dans le processus d'entraînement, d'optimisation et de production de modèles grâce à des ateliers pratiques faisant appel à Google Cloud Platform. >>> En vous inscrivant à cette spécialisation vous acceptez les conditions d'utilisation de Qwiklabs décrites dans la FAQ et disponibles à l'adresse: https://qwiklabs.com/terms_of_service <<<
该课程是 Coursera 上提供的为数不多的课程之一，目前只对已购买课程或已获得助学金的学生开放。
Qwiklabs Terms of Service
Definitions“Service” means the Lab Service and the Lab Creation Service, collectively, along with the Qwiklab Site.“Lab Service” means the educational, training, and learning services provided to you through the Qwiklabs Site, or any related website provided by Cloud vLab, in concert with your respective Lab Sponsor.“Creator role” means the designation of your account as a creator to access the “Lab Creation Service”. If you have the Creator role, all sections of this agreement apply to you including sections that reference the Lab Service and the Lab Creation Service.“Lab Creation Service” means the services and functionality hosted by Cloud vLab and made available to you only if you have a “Creator role” on or through the Qwiklabs Site through which you may deploy, configure, customize, manage, administer, and control a virtual server for implementing and testing software as a part of your training through the Lab Service.“Lab Sponsor” means the company or other organization with whom you are employed or otherwise associated in connection with the Lab Service."Content" means any content or work of authorship created, owned or licensed by you only if you have a “Creator role’, submitted to the Lab Creation Service, and that is transmitted, rendered, displayed or executed on or through the Service, including without limitation any text, postings, audio, sounds, video, photos, images, messages, software, and materials.“Sponsor Content” means any content or work of authorship created, owned, or licensed by your Lab Sponsor and utilized in the Service.“Qwiklabs Technology” means all of Cloud vLab’s proprietary technology (including, but not limited to, software, hardware, products, processes, algorithms, user interfaces, know-how, techniques, designs, and other tangible or intangible technical material or information) made available to you by Cloud vLab in providing the Service, excluding Sponsor Content.“Qwiklabs Site” means the web site located at Qwiklab.com, and/or any related or successor URLs operated or controlled by Cloud vLab.“Resources” means any virtual or physical infrastructure provided to you by the Service.Use of the ServiceOverview of Rights
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Demonstration Accounts and Use
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Intellectual PropertyCloud vLab Intellectual Property
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Term and TerminationTerm
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In no event shall Cloud vLab incur any liability to you or any End Users on account of any loss or damage resulting from any delay or failure to perform all or any part of this Agreement to the extent such delay or failure is caused by events, occurrences, or causes beyond the control and without negligence of Cloud vLab, including by not limited to acts of God, strikes, riots, acts of war, lockouts, earthquakes, fires, and explosions.
Last Updated: September 1, 2015