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.
This course is part of the Machine Learning with TensorFlow on Google Cloud en Français Specialization
Art and Science of Machine Learning en Français
About this Course
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Syllabus - What you will learn from this course
Bienvenue dans "Art and Science of Machine Learning". 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.
L'art du ML
Dans ce module, vous allez apprendre à régler la taille de lot et le taux d'apprentissage pour améliorer les performances du modèle, à optimiser votre modèle et à appliquer les concepts au code TensorFlow.
Réglages des hyperparamètres
Dans ce module, vous allez apprendre à faire la différence entre les paramètres et les hyperparamètres. Ensuite, nous aborderons l'approche traditionnelle de recherche de réseaux et verrons comment aller plus loin grâce à des algorithmes plus intelligents. Enfin, vous découvrirez à quel point Cloud ML Engine facilite l'automatisation des réglages des hyperparamètres.
Un soupçon de science
Dans ce module, nous allons évoquer la science qui accompagne l'art du machine learning. Nous allons d'abord découvrir comment effectuer la régularisation à des fins de parcimonie pour parvenir à des modèles plus simples et plus concis. Ensuite, nous verrons ce qu'est la régression logistique et comment déterminer les performances.
La science des réseaux de neurones
Dans ce module, nous allons approfondir l'aspect scientifique du ML, en particulier les réseaux de neurones.
Représentations vectorielles continues
Dans ce module, vous apprendrez à utiliser les représentations vectorielles continues pour gérer des données creuses, afin que les modèles de machine learning utilisant des données creuses consomment moins de mémoire et s'entraînent plus rapidement. Les représentations vectorielles continues permettent également d'effectuer une réduction de la dimensionnalité, et ainsi de rendre les modèles plus simples et plus généralisables.
About the Machine Learning with TensorFlow on Google Cloud en Français Specialization
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 <<<
Frequently Asked Questions
Can I preview a course before enrolling?
Yes, you can preview the first video and view the syllabus before you enroll. You must purchase the course to access content not included in the preview.
What will I get when I enroll?
Once you enroll and your session begins, you will have access to all videos and other resources, including reading items and the course discussion forum. You’ll be able to view and submit practice assessments, and complete required graded assignments to earn a grade and a Course Certificate.
When will I receive my Course Certificate?
If you complete the course successfully, your electronic Course Certificate will be added to your Accomplishments page - from there, you can print your Course Certificate or add it to your LinkedIn profile.
Why can’t I audit this course?
This course is one of a few offered on Coursera that are currently available only to learners who have paid or received financial aid, when available.
Is financial aid available?
Yes. In select learning programs, you can apply for financial aid or a scholarship if you can’t afford the enrollment fee. If fin aid or scholarship is available for your learning program selection, you’ll find a link to apply on the description page.
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Last Updated: September 1, 2015
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