Réseaux neuronaux et Deep Learning from Coursera

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.

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.

Les bases des réseaux neuronaux

Apprenez à résoudre un problème d’apprentissage automatique avec l’approche d’un réseau neuronal. Apprenez à utiliser la vectorisation pour accélérer vos modèles.

Réseaux neuronaux peu profonds

Apprenez à élaborer un réseau neuronal avec une couche cachée à l’aide de la propagation avant et de la rétropropagation.

Réseaux neuronaux profonds

Comprendre les principaux calculs sur lesquels se fonde le Deep Learning, les utiliser pour élaborer et entraîner des réseaux neuronaux profonds et les appliquer à la vision par ordinateur.

Good to know

Know what's good

, what to watch for

, 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

Reviews summary

Effective deep learning overview

Students appreciate this course's thorough, unique, and comprehensive overview of Deep Learning. They are especially appreciative of the clear explanations from the instructor, the strong foundational background that it provides, and the practical uses and applications of Deep Learning. However, some students do note that it may not be suitable for absolute beginners to this subject.

Excellent introduction to the fundamentals of deep learning.

"Cool"

Provides a solid foundation in Deep Learning.

Explanations and instruction are tremendously clear.

Learning about practical uses and applications of Deep Learning.

"Thanks to coursera for giving me the opportunity to learn more about Neural Networks in deep learning and a special thanks to Andrew , his course was so clear and generally he explained to me step by step on building my first NN program starting from the general introduction to the mathematics formulas to how identify them by coding."

May not be suitable for absolute beginners.

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 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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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