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Andrew Ng,
Kian Katanforoosh,
and
Younes Bensouda Mourri
Vous souhaitez vous lancer dans l’IA de pointe ? Ce cours est là pour vous y aider. Les ingénieurs en Deep Learning sont très convoités et la maîtrise de ce domaine vous ouvrira de nombreuses opportunités professionnelles. Le Deep Learning est également un nouveau « superpouvoir » qui vous permettra de développer des systèmes d’IA qui n’étaient même pas envisageables il y a encore quelques années.
Vous découvrirez dans ce cours les bases du Deep Learning. Une fois que vous l’aurez terminé, vous serez en mesure de :
- comprendre les grandes tendances technologiques sur lesquelles repose le Deep Learning ;
Read more
Vous souhaitez vous lancer dans l’IA de pointe ? Ce cours est là pour vous y aider. Les ingénieurs en Deep Learning sont très convoités et la maîtrise de ce domaine vous ouvrira de nombreuses opportunités professionnelles. Le Deep Learning est également un nouveau « superpouvoir » qui vous permettra de développer des systèmes d’IA qui n’étaient même pas envisageables il y a encore quelques années.
Vous découvrirez dans ce cours les bases du Deep Learning. Une fois que vous l’aurez terminé, vous serez en mesure de :
- comprendre les grandes tendances technologiques sur lesquelles repose le Deep Learning ;
Read more
Vous souhaitez vous lancer dans l’IA de pointe ? Ce cours est là pour vous y aider. Les ingénieurs en Deep Learning sont très convoités et la maîtrise de ce domaine vous ouvrira de nombreuses opportunités professionnelles. Le Deep Learning est également un nouveau « superpouvoir » qui vous permettra de développer des systèmes d’IA qui n’étaient même pas envisageables il y a encore quelques années.
Vous découvrirez dans ce cours les bases du Deep Learning. Une fois que vous l’aurez terminé, vous serez en mesure de :
- comprendre les grandes tendances technologiques sur lesquelles repose le Deep Learning ;
- développer, entraîner et utiliser des réseaux neuronaux profonds entièrement connectés ;
- mettre en œuvre des réseaux neuronaux efficaces (vectorisés) ;
- comprendre les principaux paramètres de l’architecture d’un réseau neuronal.
Ce cours ne se limitera pas à une description rapide ou superficielle du Deep Learning, mais vous expliquera également son fonctionnement. Une fois que vous l’aurez terminé, vous serez donc en mesure de l’utiliser dans vos propres applications. En outre, si vous recherchez un poste dans l’IA, vous aurez la capacité de répondre à des questions de base posées lors d’entretiens.
Il s’agit du premier cours de la Spécialisation Deep Learning.
Register for this course and see more details by visiting:
OpenCourser.com/course/wt7nio/reseaux
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What's inside
Syllabus
Introduction au Deep Learning
Être en mesure d’expliquer les grandes tendances du développement du Deep Learning et comprendre comment et dans quelles situations il est appliqué aujourd’hui.
Read more
Syllabus
Introduction au Deep Learning
Être en mesure d’expliquer les grandes tendances du développement du Deep Learning et comprendre comment et dans quelles situations il est appliqué aujourd’hui.
Read more
Traffic lights
Traffic lights
Read about what's good
what should give you pause and
possible dealbreakers
Examines Deep Learning principles, techniques, and latest trends that are transforming many industries and professions
Covers core concepts and foundational principles of Deep Learning from its inception
Taught by seasoned professionals in the field with a strong reputation, namely Andrew Ng, Younes Bensouda Mourri, and Kian Katanforoosh
Provides a cohesive foundation in Deep Learning for beginners aiming to enter the field
Requires learners to already have a base level of programming skills and some familiarity with Python
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Reviews summary
Introduction solide et claire au deep learning
Selon les apprenants, ce cours est une excellente introduction (positive) et une base solide (positive) aux Réseaux neuronaux et au Deep Learning. Le Dr. Andrew Ng (neutral) est universellement salué pour ses explications d'une clarté inégalée (positive), rendant des concepts complexes comme la vectorisation (neutral) facilement compréhensibles. Les exercices de programmation (positive) et les labs (positive) sont considérés comme très efficaces et formateurs (positive) pour solidifier la compréhension pratique. Cependant, quelques étudiants ont exprimé le désir de voir plus d'exemples concrets (warning) et moins de théorie pure, certains trouvant les exercices parfois trop guidés (warning) ou le contenu légèrement daté (warning). Malgré ces points, il reste un point de départ essentiel (positive) pour les carrières en IA.
Le cours est solide théoriquement mais certains souhaitent plus de pratique.
"J'ai apprécié ce cours, il fournit une bonne base... Cependant, j'aurais aimé voir plus d'exemples concrets ou de projets plus avancés. C'est très théorique parfois."
"Le cours est globalement bon, mais je trouve que certains exercices sont un peu trop guidés, ce qui ne laisse pas beaucoup de place à l'exploration."
"C'est une bonne introduction, mais je trouve que le cours est un peu trop orienté vers la théorie et manque d'applications pratiques concrètes pour le monde professionnel."
"Les mathématiques sont expliquées de manière accessible... Seul bémol, parfois les labs sont un peu trop 'copier-coller'."
Idéal pour les novices, potentiellement lent pour les expérimentés.
"Ce cours est une introduction fantastique au Deep Learning... Un must pour les débutants !"
"Le meilleur cours pour démarrer en Deep Learning."
"Le cours est bon si vous êtes un débutant absolu, mais si vous avez déjà quelques notions, il peut sembler trop lent et trop basique."
"J'ai un peu lutté avec certains aspects mathématiques car mon niveau n'était pas suffisant, mais le cours m'a motivé à réviser."
Les fondamentaux restent d'actualité malgré quelques points.
"Un classique et toujours aussi pertinent. Andrew Ng est le meilleur pour expliquer des concepts complexes."
"Quelques parties commencent à être légèrement datées, bien que les fondamentaux restent valides."
"Le contenu est clair et bien structuré... Pour ceux qui ont déjà des bases en machine learning, c'est une bonne mise à jour, mais pas révolutionnaire."
Les devoirs et labs renforcent la compréhension et l'application.
"Les exercices de programmation sont très utiles pour solidifier la compréhension."
"Les devoirs sont stimulants et permettent de vraiment coder."
"Les labs sont très formateurs."
"Les quiz et les exercices de programmation Jupyter sont parfaits pour renforcer les concepts."
L'instructeur est très apprécié pour sa pédagogie exceptionnelle.
"Les explications de Andrew Ng sont incroyablement claires et la manière dont il déconstruit des concepts complexes en morceaux digestes est tout simplement géniale."
"Absolument génial ! Andrew Ng rend le Deep Learning accessible à tous."
"Andrew Ng est le meilleur pour expliquer des concepts complexes."
"Andrew Ng est un professeur incroyable, ses explications sont limpides et les exercices de programmation sont très efficaces pour la pratique."
Activities
Be better prepared
before
your course. Deepen your understanding
during
and
after
it. Supplement your coursework and achieve mastery of the topics covered
in Réseaux neuronaux et Deep Learning with these
activities:
Book review: 'Deep Learning' by Ian Goodfellow et al.
Show steps
Expand your knowledge by reading a foundational book on Deep Learning.
View
L'apprentissage profond (French Edition)
on Amazon
Show steps
-
Read and summarize key chapters of the book.
-
Identify and explain the main concepts and algorithms presented in the book.
-
Discuss the strengths and weaknesses of the book and its relevance to the course.
Review of matrix operations
Show steps
Refresh your understanding of matrix operations to strengthen your foundation for Deep Learning.
Browse courses on
Linear Algebra
Show steps
-
Recall basic matrix operations, such as addition, subtraction, multiplication, and transposition.
-
Practice solving systems of linear equations using matrices.
-
Review the concepts of matrix determinants and eigenvalues.
Discussion forum participation
Show steps
Engage with fellow learners by asking questions and sharing insights in discussion forums.
Show steps
-
Post questions or comments on topics you find challenging.
-
Provide thoughtful responses to questions raised by others.
Five other activities
Expand to see all activities and additional details
Show all eight activities
Tutorial on neural network architectures
Show steps
Explore different neural network architectures and learn how they are used in practice.
Browse courses on
Neural Networks
Show steps
-
Follow a tutorial on fully connected neural networks.
-
Learn about convolutional neural networks (CNNs) and their applications in computer vision.
-
Explore recurrent neural networks (RNNs) and their use in natural language processing.
Exercises on deep learning algorithms
Show steps
Reinforce your understanding of deep learning algorithms through practice.
Show steps
-
Solve practice problems on gradient descent and backpropagation.
-
Implement different activation functions, such as sigmoid and ReLU.
-
Practice hyperparameter tuning to optimize model performance.
Contribution to open-source deep learning projects
Show steps
Contribute to the open-source community and gain valuable experience.
Browse courses on
Open Source
Show steps
-
Identify open-source deep learning projects that align with your interests.
-
Review the documentation and contribute to the project in any way you can.
-
Network with other contributors and learn from their experiences.
Project: Implement a simple deep learning model
Show steps
Apply your knowledge to build a simple deep learning model and gain practical experience.
Browse courses on
Deep Learning
Show steps
-
Choose a simple dataset and problem statement.
-
Select an appropriate deep learning model architecture.
-
Train and evaluate your model.
Project: Build a deep learning application
Show steps
Challenge yourself by building a deep learning application to showcase your skills.
Browse courses on
Artificial Intelligence
Show steps
-
Identify a real-world problem that can be addressed using deep learning.
-
Gather and prepare the necessary data.
-
Design and implement a deep learning model to solve the problem.
-
Evaluate the performance of your model and make improvements as needed.
Book review: 'Deep Learning' by Ian Goodfellow et al.
Show steps
Expand your knowledge by reading a foundational book on Deep Learning.
View
L'apprentissage profond (French Edition)
on Amazon
Show steps
Show steps
Show steps
- Read and summarize key chapters of the book.
- Identify and explain the main concepts and algorithms presented in the book.
- Discuss the strengths and weaknesses of the book and its relevance to the course.
Review of matrix operations
Show steps
Refresh your understanding of matrix operations to strengthen your foundation for Deep Learning.
Browse courses on
Linear Algebra
Show steps
Show steps
Show steps
- Recall basic matrix operations, such as addition, subtraction, multiplication, and transposition.
- Practice solving systems of linear equations using matrices.
- Review the concepts of matrix determinants and eigenvalues.
Discussion forum participation
Show steps
Engage with fellow learners by asking questions and sharing insights in discussion forums.
Show steps
Show steps
Show steps
- Post questions or comments on topics you find challenging.
- Provide thoughtful responses to questions raised by others.
Five other activities
Expand to see all activities and additional details
Show all eight activities
Tutorial on neural network architectures
Show steps
Explore different neural network architectures and learn how they are used in practice.
Browse courses on
Neural Networks
Show steps
Show steps
Show steps
- Follow a tutorial on fully connected neural networks.
- Learn about convolutional neural networks (CNNs) and their applications in computer vision.
- Explore recurrent neural networks (RNNs) and their use in natural language processing.
Exercises on deep learning algorithms
Show steps
Reinforce your understanding of deep learning algorithms through practice.
Show steps
Show steps
Show steps
- Solve practice problems on gradient descent and backpropagation.
- Implement different activation functions, such as sigmoid and ReLU.
- Practice hyperparameter tuning to optimize model performance.
Contribution to open-source deep learning projects
Show steps
Contribute to the open-source community and gain valuable experience.
Browse courses on
Open Source
Show steps
Show steps
Show steps
- Identify open-source deep learning projects that align with your interests.
- Review the documentation and contribute to the project in any way you can.
- Network with other contributors and learn from their experiences.
Project: Implement a simple deep learning model
Show steps
Apply your knowledge to build a simple deep learning model and gain practical experience.
Browse courses on
Deep Learning
Show steps
Show steps
Show steps
- Choose a simple dataset and problem statement.
- Select an appropriate deep learning model architecture.
- Train and evaluate your model.
Project: Build a deep learning application
Show steps
Challenge yourself by building a deep learning application to showcase your skills.
Browse courses on
Artificial Intelligence
Show steps
Show steps
Show steps
- Identify a real-world problem that can be addressed using deep learning.
- Gather and prepare the necessary data.
- Design and implement a deep learning model to solve the problem.
- Evaluate the performance of your model and make improvements as needed.
Career center
Learners who complete Réseaux neuronaux et Deep Learning will develop knowledge and skills
that may be useful to these careers:
Data Scientist
Data Scientist
Data Scientists design and implement data mining algorithms, analyze data using statistical techniques, and interpret and present results to help businesses make informed decisions. This course provides a foundation in deep learning, a powerful technique used in data science. By learning the basics of deep learning, Data Scientists can develop and implement more sophisticated models to derive insights from data.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers design, develop, and deploy machine learning models. They use deep learning techniques to build models that can learn from data and make predictions. This course provides a solid foundation in deep learning, which is essential for Machine Learning Engineers. By completing this course, Machine Learning Engineers will be able to develop and implement deep learning models to solve complex problems.
Deep Learning Engineer
Deep Learning Engineer
Deep Learning Engineers specialize in developing and implementing deep learning models. They use deep learning techniques to solve complex problems in various domains, such as computer vision, natural language processing, and speech recognition. This course provides a comprehensive foundation in deep learning, which is essential for Deep Learning Engineers. By completing this course, Deep Learning Engineers will be able to develop and implement deep learning models to solve real-world problems.
Computer Vision Engineer
Computer Vision Engineer
Computer Vision Engineers design and develop computer vision systems that can interpret and understand visual data. They use deep learning techniques to build models that can recognize objects, detect patterns, and track movement. This course provides a strong foundation in deep learning, which is essential for Computer Vision Engineers. By completing this course, Computer Vision Engineers will be able to develop and implement deep learning models to solve complex problems in computer vision.
Natural Language Processing Engineer
Natural Language Processing Engineer
Natural Language Processing Engineers design and develop natural language processing systems that can understand and generate human language. They use deep learning techniques to build models that can translate languages, answer questions, and generate text. This course provides a strong foundation in deep learning, which is essential for Natural Language Processing Engineers. By completing this course, Natural Language Processing Engineers will be able to develop and implement deep learning models to solve complex problems in natural language processing.
Speech Recognition Engineer
Speech Recognition Engineer
Speech Recognition Engineers design and develop speech recognition systems that can recognize and transcribe human speech. They use deep learning techniques to build models that can understand different accents, dialects, and noise levels. This course provides a strong foundation in deep learning, which is essential for Speech Recognition Engineers. By completing this course, Speech Recognition Engineers will be able to develop and implement deep learning models to solve complex problems in speech recognition.
Software Engineer
Software Engineer
Software Engineers design, develop, and maintain software systems. They use deep learning techniques to build software systems that can learn from data and make predictions. This course provides a foundation in deep learning, which is increasingly used in software development. By completing this course, Software Engineers will be able to develop and implement deep learning models to enhance their software systems.
Data Analyst
Data Analyst
Data Analysts collect, clean, and analyze data to help businesses make informed decisions. They use deep learning techniques to build models that can identify trends, patterns, and anomalies in data. This course provides a foundation in deep learning, which can be helpful for Data Analysts looking to enhance their skills in data analysis. By completing this course, Data Analysts will be able to develop and implement deep learning models to solve complex problems in data analysis.
Business Analyst
Business Analyst
Business Analysts analyze business processes and systems to identify areas for improvement. They use deep learning techniques to build models that can optimize business processes and increase efficiency. This course provides a foundation in deep learning, which may be helpful for Business Analysts looking to expand their skill set. By completing this course, Business Analysts will be able to develop and implement deep learning models to solve complex problems in business analysis.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts use mathematical and statistical techniques to analyze financial data and make investment decisions. They use deep learning techniques to build models that can predict financial trends and identify investment opportunities. This course provides a foundation in deep learning, which may be helpful for Quantitative Analysts looking to expand their skill set. By completing this course, Quantitative Analysts will be able to develop and implement deep learning models to solve complex problems in quantitative analysis.
Market Researcher
Market Researcher
Market Researchers conduct research to understand market trends and consumer behavior. They use deep learning techniques to build models that can identify customer needs and preferences. This course provides a foundation in deep learning, which may be helpful for Market Researchers looking to expand their skill set. By completing this course, Market Researchers will be able to develop and implement deep learning models to solve complex problems in market research.
Product Manager
Product Manager
Product Managers develop and manage products from concept to launch. They use deep learning techniques to build models that can predict customer demand and optimize product features. This course provides a foundation in deep learning, which may be helpful for Product Managers looking to expand their skill set. By completing this course, Product Managers will be able to develop and implement deep learning models to solve complex problems in product management.
Operations Research Analyst
Operations Research Analyst
Operations Research Analysts use mathematical and statistical techniques to optimize business operations. They use deep learning techniques to build models that can improve supply chain management, scheduling, and resource allocation. This course provides a foundation in deep learning, which may be helpful for Operations Research Analysts looking to expand their skill set. By completing this course, Operations Research Analysts will be able to develop and implement deep learning models to solve complex problems in operations research.
Risk Analyst
Risk Analyst
Risk Analysts use mathematical and statistical techniques to assess and manage risk. They use deep learning techniques to build models that can predict the likelihood and impact of risks. This course provides a foundation in deep learning, which may be helpful for Risk Analysts looking to expand their skill set. By completing this course, Risk Analysts will be able to develop and implement deep learning models to solve complex problems in risk analysis.
Statistician
Statistician
Statisticians use mathematical and statistical techniques to collect, analyze, and interpret data. They use deep learning techniques to build models that can identify trends, patterns, and anomalies in data. This course provides a foundation in deep learning, which may be helpful for Statisticians looking to expand their skill set. By completing this course, Statisticians will be able to develop and implement deep learning models to solve complex problems in statistics.
For more career information including salaries, visit:
OpenCourser.com/course/wt7nio/reseaux
Reading list
We've selected 11 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
Réseaux neuronaux et Deep Learning.
Save
Cet ouvrage complet couvre les bases théoriques et pratiques du Deep Learning. Il est considéré comme une référence dans le domaine et convient parfaitement aux étudiants et aux praticiens souhaitant approfondir leurs connaissances en Deep Learning.
Ce livre fournit un guide pratique pour implémenter des algorithmes de Machine Learning en utilisant des bibliothèques populaires telles que Scikit-Learn, Keras et TensorFlow. Il peut être utile aux étudiants souhaitant acquérir une expérience pratique en Deep Learning.
Ce livre se concentre sur l'implémentation pratique du Deep Learning en utilisant Python et la bibliothèque Keras. Il est recommandé aux étudiants ayant quelques connaissances préalables en Python et souhaitant se spécialiser dans l'aspect pratique du Deep Learning.
Ce livre classique couvre un large éventail de sujets liés au Machine Learning, y compris les réseaux neuronaux. Il fournit une introduction complète aux concepts théoriques et pratiques du Deep Learning, mais son niveau de difficulté peut être élevé pour les débutants.
Save
Ce livre en français fournit une introduction complète au Deep Learning en utilisant la langage R. Il convient aux étudiants francophones souhaitant acquérir une compréhension approfondie des concepts et des techniques du Deep Learning.
Ce livre couvre spécifiquement l'application du Deep Learning au traitement du langage naturel. Il s'adresse aux étudiants souhaitant se spécialiser dans ce domaine et approfondir leur compréhension des techniques de traitement du langage naturel.
Save
Ce livre fournit une introduction complète aux réseaux adversaires génératifs (GAN), qui sont des architectures de réseau neuronal utilisées pour générer de nouvelles données. Il convient aux étudiants souhaitant approfondir leurs connaissances dans ce domaine.
Ce livre classique introduit les concepts de base de l'apprentissage par renforcement, qui est une technique de Deep Learning utilisée pour entraîner des agents à prendre des décisions optimales dans des environnements complexes. Il convient aux étudiants souhaitant approfondir leur compréhension de ce domaine.
Ce livre fournit une introduction théorique au Machine Learning et couvre les concepts mathématiques sous-jacents aux algorithmes de Deep Learning. Il s'adresse aux étudiants souhaitant acquérir une compréhension approfondie des fondements théoriques du Deep Learning.
Ce livre fournit une introduction aux modèles graphiques probabilistes, qui sont largement utilisés dans le Deep Learning pour représenter les relations entre les variables. Il convient aux étudiants souhaitant acquérir une compréhension approfondie des fondements théoriques du Deep Learning.
Ce livre introduit les concepts du raisonnement bayésien et leur application au Machine Learning. Il fournit une base théorique solide pour comprendre les méthodes de Deep Learning basées sur les probabilités.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/wt7nio/reseaux
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Rating
4.9
Effort
19 hours to complete
Level
Intermediate
Via
Coursera
Institution
DeepLearning.AI
Instructors
Andrew Ng
Kian Katanforoosh
Younes Bensouda Mourri
Language
Français
Transcripts
English
Español
Português
Deutsch
中文
العربية
한국어
日本語
Türkçe
українська мова
tiếng Việt
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