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Google Cloud Training
Ce cours offre un aperçu de l'architecture encodeur/décodeur, une architecture de machine learning performante souvent utilisée pour les tâches "seq2seq", telles que la traduction automatique, la synthèse de texte et les questions-réponses. Vous découvrirez quels sont les principaux composants de l'architecture encodeur/décodeur, et comment entraîner et exécuter ces modèles. Dans le tutoriel d'atelier correspondant, vous utiliserez TensorFlow pour coder une implémentation simple de cette architecture afin de générer un poème en partant de zéro.
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What's inside
Syllabus
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Introduces learners to the Encoder-decoder architecture, which is a proven and reliable approach for 'seq2seq' tasks in Machine Learning
The course has a practical focus, with a hands-on TensorFlow tutorial that lets learners implement their own Encoder-decoder model to generate poems
Taught by experts from Google Cloud Training, who have a strong reputation in the field of Machine Learning
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Reviews summary
Architecture encodeur-décodeur : aperçu pratique
Selon les apprenants, ce cours sur l'architecture encodeur-décodeur de Google Cloud est une introduction exceptionnellement claire et concise aux concepts fondamentaux des modèles "seq2seq". Les explications sont jugées faciles à suivre, ce qui en fait un excellent point de départ pour les débutants en apprentissage automatique. Le tutoriel pratique avec TensorFlow, qui permet de générer un poème, est souvent cité comme un atout majeur, offrant une application concrète des connaissances. Cependant, certains étudiants notent que la couverture est relativement rapide et que le cours pourrait manquer de profondeur pour les apprenants plus expérimentés cherchant des détails avancés.
Excellent point de départ pour les novices en ML.
"En tant que débutant en machine learning, j'ai trouvé ce cours parfaitement adapté pour m'initier à l'architecture."
"Ce cours est une première étape très accessible pour comprendre les bases de la traduction automatique et de la synthèse de texte."
"Je recommande ce cours à quiconque cherche une introduction solide et non intimidante à ces architectures."
Permet une application concrète des connaissances acquises.
"Le laboratoire TensorFlow est un excellent moyen de mettre en pratique ce que l'on apprend, j'ai aimé coder mon poème."
"J'ai trouvé le tutoriel de codage très utile pour solidifier ma compréhension des concepts théoriques du cours."
"L'aspect pratique avec TensorFlow a vraiment rendu le sujet plus tangible et m'a aidé à visualiser l'implémentation."
Offre une introduction nette aux concepts clés.
"J'ai trouvé la présentation de l'architecture encodeur-décodeur très claire et facile à suivre pour un débutant."
"Le cours explique les concepts fondamentaux de manière concise et compréhensible, c'est idéal pour une première approche."
"J'ai apprécié la clarté des explications sur les composants et le fonctionnement des modèles seq2seq."
Peut manquer de détails pour les apprenants avancés.
"J'aurais aimé que le cours explore des sujets plus avancés ou des optimisations, il reste très introductif."
"Pour quelqu'un avec une bonne base en ML, ce cours peut sembler un peu trop superficiel et rapide."
"C'est un bon aperçu, mais ne vous attendez pas à une plongée en profondeur dans les subtilités de l'architecture."
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Compiler vos notes et autres matériels vous aidera à organiser et à renforcer vos connaissances sur les concepts clés de l'architecture Encodeur/Décodeur.
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Lire le livre Deep Learning
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Ce livre vous fournira une base solide dans les concepts fondamentaux du Deep Learning, vous préparant ainsi à réussir dans ce cours.
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L'apprentissage profond (French Edition)
on Amazon
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Show steps
Show steps
- Lire les chapitres 1 à 5
- Résoudre les exercices proposés à la fin de chaque chapitre
- Discuter des concepts clés avec des camarades de classe ou des mentors
Suivre les tutoriels TensorFlow
Show steps
Ces tutoriels vous permettront de maîtriser TensorFlow, la bibliothèque de Deep Learning utilisée dans ce cours, renforçant ainsi vos compétences pratiques.
Browse courses on
TensorFlow
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Show steps
Show steps
- Suivre le tutoriel de base de TensorFlow
- Créer un modèle d'apprentissage automatique simple à l'aide de TensorFlow
- Explorer les ressources supplémentaires et les exemples fournis par TensorFlow
Compiler les notes, les devoirs et les examens
Show steps
Compiler vos notes et autres matériels vous aidera à organiser et à renforcer vos connaissances sur les concepts clés de l'architecture Encodeur/Décodeur.
Show steps
Show steps
Show steps
- Rassembler toutes les notes, devoirs et examens
- Organiser les documents par sujet
- Annoter et compléter les notes avec des informations supplémentaires
Show all three activities
Career center
Learners who complete Encoder-Decoder Architecture - Français will develop knowledge and skills
that may be useful to these careers:
Artificial Intelligence Engineer
Artificial Intelligence Engineer
Artificial Intelligence Engineers design, develop, and maintain artificial intelligence systems. They work with a variety of machine learning algorithms and techniques to create systems that can perform tasks such as natural language processing, computer vision, and robotics. This course provides an overview of the encoder-decoder architecture, which is a powerful machine learning architecture often used for tasks such as natural language processing and computer vision. This course may be useful for you if you are interested in a career in artificial intelligence engineering.
Machine Learning Researcher
Machine Learning Researcher
Machine Learning Researchers develop new machine learning algorithms and techniques. They work with a variety of data sets and machine learning models to improve the performance of machine learning systems. This course provides an overview of the encoder-decoder architecture, which is a powerful machine learning architecture often used for tasks such as natural language processing and computer vision. This course may be useful for you if you are interested in a career in machine learning research.
Natural Language Processing Engineer
Natural Language Processing Engineer
Natural Language Processing Engineers develop and maintain software systems that can understand and generate human language. They work with a variety of machine learning algorithms and techniques to create systems that can perform tasks such as machine translation, text summarization, and question answering. This course provides an overview of the encoder-decoder architecture, which is a powerful machine learning architecture often used for natural language processing tasks. This course may be useful for you if you are interested in a career in natural language processing.
Computer Vision Engineer
Computer Vision Engineer
Computer Vision Engineers develop and maintain software systems that can understand and interpret images and videos. They work with a variety of machine learning algorithms and techniques to create systems that can perform tasks such as object detection, image classification, and video analysis. This course provides an overview of the encoder-decoder architecture, which is a powerful machine learning architecture often used for computer vision tasks. This course may be useful for you if you are interested in a career in computer vision.
Research Scientist
Research Scientist
Research Scientists conduct research in a variety of fields, including machine learning, natural language processing, and computer vision. They develop new algorithms and techniques to solve challenging problems. This course provides an overview of the encoder-decoder architecture, which is a powerful machine learning architecture often used for tasks such as natural language processing and computer vision. This course may be useful for you if you are interested in a career in research.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers are responsible for the development and deployment of machine learning models. As a Machine Learning Engineer, you will need to have a strong understanding of machine learning algorithms, data analysis, and software engineering principles. This course provides an overview of the encoder-decoder architecture, which is a powerful machine learning architecture often used for tasks such as natural language processing and computer vision. This course may be useful for you if you are interested in a career in machine learning engineering.
Data Scientist
Data Scientist
Data Scientists use data to solve business problems. They collect, clean, and analyze data to identify patterns and trends. This course provides an overview of the encoder-decoder architecture, which is a powerful machine learning architecture often used for tasks such as natural language processing and computer vision. This course may be useful for you if you are interested in a career in data science.
Data Analyst
Data Analyst
Data Analysts collect, clean, and analyze data to identify patterns and trends. They work with a variety of data analysis tools and techniques to create reports and visualizations that can be used to make informed decisions. This course provides an overview of the encoder-decoder architecture, which is a powerful machine learning architecture often used for tasks such as natural language processing and computer vision. This course may be useful for you if you are interested in a career in data analysis.
Software Engineer
Software Engineer
Software Engineers design, develop, and maintain software applications. They work with a variety of programming languages and technologies to create software that meets the needs of users. This course provides an overview of the encoder-decoder architecture, which is a powerful machine learning architecture often used for tasks such as natural language processing and computer vision. This course may be useful for you if you are interested in a career in software engineering.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts use mathematical and statistical models to analyze financial data. They work with a variety of data analysis tools and techniques to identify investment opportunities and manage risk. This course provides an overview of the encoder-decoder architecture, which is a powerful machine learning architecture often used for tasks such as natural language processing and computer vision. This course may be useful for you if you are interested in a career in quantitative analysis.
Actuary
Actuary
Actuaries use mathematical and statistical models to assess risk and uncertainty. They work with a variety of data analysis tools and techniques to develop insurance products and pricing models. This course provides an overview of the encoder-decoder architecture, which is a powerful machine learning architecture often used for tasks such as natural language processing and computer vision. This course may be useful for you if you are interested in a career in actuarial science.
Operations Research Analyst
Operations Research Analyst
Operations Research Analysts use mathematical and statistical models to improve the efficiency of business operations. They work with a variety of data analysis tools and techniques to develop solutions to problems such as supply chain management, logistics, and scheduling. This course provides an overview of the encoder-decoder architecture, which is a powerful machine learning architecture often used for tasks such as natural language processing and computer vision. This course may be useful for you if you are interested in a career in operations research.
Business Analyst
Business Analyst
Business Analysts use data analysis techniques to identify and solve business problems. They work with a variety of stakeholders to understand business needs and develop solutions that meet those needs. This course provides an overview of the encoder-decoder architecture, which is a powerful machine learning architecture often used for tasks such as natural language processing and computer vision. This course may be useful for you if you are interested in a career in business analysis.
Product Manager
Product Manager
Product Managers are responsible for the development and marketing of new products. They work with a variety of stakeholders to understand customer needs and develop products that meet those needs. This course provides an overview of the encoder-decoder architecture, which is a powerful machine learning architecture often used for tasks such as natural language processing and computer vision. This course may be useful for you if you are interested in a career in product management.
Marketing Manager
Marketing Manager
Marketing Managers are responsible for the development and execution of marketing campaigns. They work with a variety of stakeholders to understand customer needs and develop campaigns that reach those customers. This course provides an overview of the encoder-decoder architecture, which is a powerful machine learning architecture often used for tasks such as natural language processing and computer vision. This course may be useful for you if you are interested in a career in marketing management.
For more career information including salaries, visit:
OpenCourser.com/course/h04nn9/encoder
Reading list
We've selected eight books
that we think will supplement your
learning. Use these to
develop background knowledge, enrich your coursework, and gain a
deeper understanding of the topics covered in
Encoder-Decoder Architecture - Français.
Provides a comprehensive overview of deep learning for natural language processing, covering a wide range of topics from foundational concepts to advanced research directions.
This textbook covers a comprehensive range of core NLP topics, including speech recognition, text processing, digital text libraries, natural language generation, and machine translation.
Provides a detailed overview of statistical machine translation, a traditional approach to machine translation that has been largely replaced by neural machine translation.
Provides a practical guide to natural language processing with PyTorch, covering a wide range of topics from foundational concepts to advanced research directions.
Provides a comprehensive overview of deep learning with Python, covering a wide range of topics from foundational concepts to advanced research directions.
This textbook covers a comprehensive range of machine learning topics, including supervised and unsupervised learning, dimensionality reduction, and Bayesian inference.
This textbook covers a comprehensive range of machine learning topics, including probability theory, information theory, and Bayesian inference.
This textbook covers a comprehensive range of machine learning topics, including supervised and unsupervised learning, regularization techniques, and model selection.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/h04nn9/encoder
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