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Ce cours présente les concepts d'interprétabilité et de transparence de l'IA. Il explique en quoi la transparence de l'IA est importante pour les développeurs et les ingénieurs. Il explore des méthodes et des outils pratiques permettant d'atteindre l'interprétabilité et la transparence des modèles d'IA et des données.
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What's inside
Syllabus
Présentation du cours
Ce module présente la structure du cours et ses objectifs.
Interprétabilité et transparence de l'IA
Ce module s'intéresse principalement à l'interprétabilité et la transparence de l'IA. Il fournit des techniques et outils différents permettant d'atteindre l'interprétabilité et la transparence des modèles d'IA et des données.
Read more
Syllabus
Présentation du cours
Ce module présente la structure du cours et ses objectifs.
Interprétabilité et transparence de l'IA
Ce module s'intéresse principalement à l'interprétabilité et la transparence de l'IA. Il fournit des techniques et outils différents permettant d'atteindre l'interprétabilité et la transparence des modèles d'IA et des données.
Read more
Résumé du cours
Ce module propose une synthèse du cours articulée autour des concepts, technologies et outils les plus importants.
Ressources du cours
Le PDF destiné aux participants contient les liens vers tous les modules.
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Développe des compétences fondamentales pour interpréter et rendre transparents les modèles et les données d'IA
Présenté par Google Cloud Training, un leader reconnu dans le domaine de l'IA
Convient aux développeurs et aux ingénieurs qui souhaitent améliorer l'interprétabilité et la transparence de leurs modèles d'IA
Couvre les concepts, techniques et outils essentiels pour rendre les modèles d'IA interprétables et transparents
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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 Responsible AI for Developers: Interpretability & Transparency - Français with these
activities:
Review concepts of interpretability and transparency in AI
Show steps
Refresh your understanding of core AI principles to strengthen your foundation for this course.
Browse courses on
Machine Learning
Show steps
-
Review notes or textbook chapters on interpretability and transparency in AI
-
Summarize key concepts and definitions related to interpretability and transparency
-
Identify practical examples of interpretable and transparent AI systems
Review concepts of AI
Show steps
Brush up on the fundamentals of AI and machine learning to strengthen your understanding of topics covered in this course.
Browse courses on
AI
Show steps
-
Review key concepts of AI, such as algorithms, data structures, and AI techniques.
-
Practice implementing basic AI algorithms.
Participate in a study group or discussion forum on AI interpretability
Show steps
Engage with peers to exchange knowledge, clarify concepts, and broaden your understanding of AI interpretability.
Browse courses on
Machine Learning
Show steps
-
Join an online study group or discussion forum focused on AI interpretability
-
Actively participate in discussions, asking questions, sharing insights, and providing feedback
-
Collaborate with others to explore different perspectives and approaches
Eight other activities
Expand to see all activities and additional details
Show all 11 activities
Explore tools for AI transparency
Show steps
Expand your knowledge by exploring different tools and techniques used to ensure AI transparency.
Show steps
-
Research and identify available tools for AI transparency.
-
Follow tutorials and documentation to learn how to use these tools effectively.
-
Test and experiment with different tools to gain practical experience.
Practice using methods and tools for AI interpretability
Show steps
Apply and reinforce your understanding of interpretability methods and tools through hands-on practice.
Browse courses on
Machine Learning
Show steps
-
Explore online resources or tutorials on interpretability methods and tools
-
Implement interpretability techniques on sample AI models
-
Analyze and interpret the results to gain insights into model behavior
Practice interpreting AI models
Show steps
Engage in hands-on exercises to develop your skills in interpreting and understanding the output of AI models.
Show steps
-
Analyze the results of different AI models.
-
Identify patterns and draw insights from AI model outputs.
Participate in AI transparency discussion groups
Show steps
Engage with peers by joining discussion groups to share ideas, ask questions, and learn from others interested in AI transparency.
Show steps
-
Join online or offline discussion groups focused on AI transparency.
-
Actively participate in discussions, sharing your perspectives and insights.
-
Learn from the experiences and knowledge of other participants.
Participate in AI transparency competitions
Show steps
Challenge yourself by participating in competitions that focus on developing AI models with high levels of transparency.
Show steps
-
Identify relevant competitions in the field of AI transparency.
-
Form a team or collaborate with others to develop a competitive solution.
-
Work on your project and submit it for evaluation.
Create a blog post or article on AI interpretability and transparency
Show steps
Demonstrate your understanding and engage with the topic by creating a written piece about AI interpretability and transparency.
Show steps
-
Identify a specific aspect or issue related to AI interpretability and transparency
-
Research the topic thoroughly, gathering information from reputable sources
-
Develop a clear and concise outline for your blog post or article
-
Write the content, explaining the concepts and providing examples
-
Proofread and edit your work for clarity and accuracy
Create a presentation on AI transparency
Show steps
Demonstrate your understanding by creating a comprehensive presentation that explores the importance of AI transparency.
Show steps
-
Research best practices and case studies of AI transparency.
-
Develop a storyboard and outline for your presentation.
-
Create visually appealing slides and engaging content.
-
Practice delivering your presentation effectively.
Contribute to open-source projects related to AI interpretability
Show steps
Apply your skills and contribute to the advancement of AI interpretability by participating in open-source initiatives.
Show steps
-
Identify open-source projects or initiatives related to AI interpretability
-
Review the project documentation and codebase to understand its purpose and functionality
-
Identify areas where you can contribute, such as bug fixes, feature enhancements, or documentation improvements
-
Submit your contributions and engage with the project maintainers
Review concepts of interpretability and transparency in AI
Show steps
Refresh your understanding of core AI principles to strengthen your foundation for this course.
Browse courses on
Machine Learning
Show steps
Show steps
Show steps
- Review notes or textbook chapters on interpretability and transparency in AI
- Summarize key concepts and definitions related to interpretability and transparency
- Identify practical examples of interpretable and transparent AI systems
Review concepts of AI
Show steps
Brush up on the fundamentals of AI and machine learning to strengthen your understanding of topics covered in this course.
Browse courses on
AI
Show steps
Show steps
Show steps
- Review key concepts of AI, such as algorithms, data structures, and AI techniques.
- Practice implementing basic AI algorithms.
Participate in a study group or discussion forum on AI interpretability
Show steps
Engage with peers to exchange knowledge, clarify concepts, and broaden your understanding of AI interpretability.
Browse courses on
Machine Learning
Show steps
Show steps
Show steps
- Join an online study group or discussion forum focused on AI interpretability
- Actively participate in discussions, asking questions, sharing insights, and providing feedback
- Collaborate with others to explore different perspectives and approaches
Eight other activities
Expand to see all activities and additional details
Show all 11 activities
Explore tools for AI transparency
Show steps
Expand your knowledge by exploring different tools and techniques used to ensure AI transparency.
Show steps
Show steps
Show steps
- Research and identify available tools for AI transparency.
- Follow tutorials and documentation to learn how to use these tools effectively.
- Test and experiment with different tools to gain practical experience.
Practice using methods and tools for AI interpretability
Show steps
Apply and reinforce your understanding of interpretability methods and tools through hands-on practice.
Browse courses on
Machine Learning
Show steps
Show steps
Show steps
- Explore online resources or tutorials on interpretability methods and tools
- Implement interpretability techniques on sample AI models
- Analyze and interpret the results to gain insights into model behavior
Practice interpreting AI models
Show steps
Engage in hands-on exercises to develop your skills in interpreting and understanding the output of AI models.
Show steps
Show steps
Show steps
- Analyze the results of different AI models.
- Identify patterns and draw insights from AI model outputs.
Participate in AI transparency discussion groups
Show steps
Engage with peers by joining discussion groups to share ideas, ask questions, and learn from others interested in AI transparency.
Show steps
Show steps
Show steps
- Join online or offline discussion groups focused on AI transparency.
- Actively participate in discussions, sharing your perspectives and insights.
- Learn from the experiences and knowledge of other participants.
Participate in AI transparency competitions
Show steps
Challenge yourself by participating in competitions that focus on developing AI models with high levels of transparency.
Show steps
Show steps
Show steps
- Identify relevant competitions in the field of AI transparency.
- Form a team or collaborate with others to develop a competitive solution.
- Work on your project and submit it for evaluation.
Create a blog post or article on AI interpretability and transparency
Show steps
Demonstrate your understanding and engage with the topic by creating a written piece about AI interpretability and transparency.
Show steps
Show steps
Show steps
- Identify a specific aspect or issue related to AI interpretability and transparency
- Research the topic thoroughly, gathering information from reputable sources
- Develop a clear and concise outline for your blog post or article
- Write the content, explaining the concepts and providing examples
- Proofread and edit your work for clarity and accuracy
Create a presentation on AI transparency
Show steps
Demonstrate your understanding by creating a comprehensive presentation that explores the importance of AI transparency.
Show steps
Show steps
Show steps
- Research best practices and case studies of AI transparency.
- Develop a storyboard and outline for your presentation.
- Create visually appealing slides and engaging content.
- Practice delivering your presentation effectively.
Contribute to open-source projects related to AI interpretability
Show steps
Apply your skills and contribute to the advancement of AI interpretability by participating in open-source initiatives.
Show steps
Show steps
Show steps
- Identify open-source projects or initiatives related to AI interpretability
- Review the project documentation and codebase to understand its purpose and functionality
- Identify areas where you can contribute, such as bug fixes, feature enhancements, or documentation improvements
- Submit your contributions and engage with the project maintainers
Career center
Learners who complete Responsible AI for Developers: Interpretability & Transparency - Français will develop knowledge and skills
that may be useful to these careers:
For more career information including salaries, visit:
OpenCourser.com/course/u10hv5/responsible
Reading list
We haven't picked any books for this reading list yet.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/u10hv5/responsible
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Level
Intermediate
Via
Coursera
Institution
Google Cloud
Instructor
Google Cloud Training
Session
August 19, 2024
- September 23, 2024
Language
Français
Good to know
Développe des compétences fondamentales pour interpréter et rendre transparents les modèles et les données d'IA
Présenté par Google Cloud Training, un leader reconnu dans le domaine de l'IA
Convient aux développeurs et aux ingénieurs qui souhaitent améliorer l'interprétabilité et la transparence de leurs modèles d'IA
Couvre les concepts, techniques et outils essentiels pour rendre les modèles d'IA interprétables et transparents
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