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Neste curso, apresentamos os conceitos de interpretabilidade e transparência em IA. Vamos abordar a importância da transparência em IA para desenvolvedores e engenheiros. O curso também abrange ferramentas e métodos práticos para ajudar a alcançar a interpretabilidade e a transparência em dados e modelos de IA.
Register for this course and see more details by visiting:
OpenCourser.com/course/ruakvs/responsible
What's inside
Syllabus
Introdução ao curso
Neste módulo, apresentamos a estrutura e os objetivos do curso.
Interpretabilidade e transparência em IA
O foco deste módulo é a interpretabilidade e a transparência em IA. Ele contém várias técnicas e ferramentas que ajudam a alcançar a interpretabilidade e a transparência em dados e modelos de IA.
Read more
Syllabus
Introdução ao curso
Neste módulo, apresentamos a estrutura e os objetivos do curso.
Interpretabilidade e transparência em IA
O foco deste módulo é a interpretabilidade e a transparência em IA. Ele contém várias técnicas e ferramentas que ajudam a alcançar a interpretabilidade e a transparência em dados e modelos de IA.
Read more
Resumo do curso
Neste módulo, fornecemos um resumo de todo o curso abordando os conceitos, as ferramentas e as tecnologias mais importantes.
Recursos do curso
Links para todos os módulos no PDF do estudante
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Ensina tópicos relevantes para desenvolvedores e engenheiros que precisam de conhecimento em Inteligência Artificial (IA)
Fornece ferramentas e métodos práticos para interpretabilidade e transparência em dados e modelos de IA
Explora a importância da transparência em IA, o que é fundamental para o desenvolvimento ético e responsável de sistemas de IA
Save this course
Save Responsible AI for Developers: Interpretability & Transparency - Português Brasileiro to your list so you can find it easily later:
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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 - Português Brasileiro with these
activities:
Revise AI Fundamentals
Show steps
Review basic AI concepts to ensure a strong foundation for the course.
Show steps
-
Read through your notes or textbooks from previous AI courses.
-
Go over online resources and tutorials on AI fundamentals.
-
Complete practice problems or quizzes to test your understanding.
Seek Mentorship from Experts in Interpretable AI
Show steps
Connect with experienced professionals to gain personalized guidance and insights in interpretable AI.
Show steps
-
Identify potential mentors who are actively involved in research or industry applications of interpretable AI.
-
Reach out to them via email, LinkedIn, or other professional networking platforms.
-
Express your interest in learning from their expertise and request their guidance.
-
Schedule regular meetings or connect virtually to discuss your progress and seek advice.
Join a Study Group for Collaborative Learning
Show steps
Engage with peers to discuss course concepts, share insights, and support each other's learning.
Show steps
-
Reach out to classmates or join online forums to find individuals interested in forming a study group.
-
Establish regular meeting times and create a shared workspace for collaboration.
-
Take turns presenting concepts, leading discussions, and facilitating group activities.
-
Work together to solve problems, clarify doubts, and reinforce your understanding.
Four other activities
Expand to see all activities and additional details
Show all seven activities
Follow Guided Tutorials on Interpretability and Transparency in AI
Show steps
Expand your understanding of the concepts covered in the course through guided tutorials.
Browse courses on
Explainable AI
Show steps
-
Identify reputable online platforms or resources that offer guided tutorials on interpretability and transparency in AI.
-
Choose tutorials that align with the course objectives and your learning goals.
-
Follow the tutorials step-by-step, taking notes and experimenting with the provided examples.
-
Seek clarification or ask questions in online forums or discussion groups.
Practice Interpreting Real-World AI Applications
Show steps
Develop your skills in interpreting AI models by working on practical examples.
Browse courses on
AI Applications
Show steps
-
Identify real-world AI applications in various domains, such as healthcare, finance, or manufacturing.
-
Analyze the AI models used in these applications and their impact on decision-making.
-
Evaluate the interpretability and transparency of these models, considering factors such as explainability and fairness.
-
Discuss your findings and insights with peers or online communities.
Create a Visualization Tool for Interpreting AI Models
Show steps
Reinforce your understanding by creating a tool that visualizes the behavior and predictions of AI models.
Browse courses on
Model Interpretability
Show steps
-
Choose an AI model and dataset that you are familiar with.
-
Identify the key features and patterns in the data that you want to visualize.
-
Design and implement a visualization tool using programming languages or software.
-
Test and refine your tool to ensure it effectively conveys the interpretability of the AI model.
Contribute to Open-Source Projects on Interpretable AI
Show steps
Gain practical experience and contribute to the advancement of interpretable AI by participating in open-source projects.
Browse courses on
Community Involvement
Show steps
-
Explore open-source repositories on platforms like GitHub or GitLab for projects related to interpretable AI.
-
Identify areas where you can contribute based on your skills and interests.
-
Familiarize yourself with the project's codebase and documentation.
-
Make code contributions, report bugs, or suggest improvements to enhance the interpretability of the AI models.
Revise AI Fundamentals
Show steps
Review basic AI concepts to ensure a strong foundation for the course.
Show steps
Show steps
Show steps
- Read through your notes or textbooks from previous AI courses.
- Go over online resources and tutorials on AI fundamentals.
- Complete practice problems or quizzes to test your understanding.
Seek Mentorship from Experts in Interpretable AI
Show steps
Connect with experienced professionals to gain personalized guidance and insights in interpretable AI.
Show steps
Show steps
Show steps
- Identify potential mentors who are actively involved in research or industry applications of interpretable AI.
- Reach out to them via email, LinkedIn, or other professional networking platforms.
- Express your interest in learning from their expertise and request their guidance.
- Schedule regular meetings or connect virtually to discuss your progress and seek advice.
Join a Study Group for Collaborative Learning
Show steps
Engage with peers to discuss course concepts, share insights, and support each other's learning.
Show steps
Show steps
Show steps
- Reach out to classmates or join online forums to find individuals interested in forming a study group.
- Establish regular meeting times and create a shared workspace for collaboration.
- Take turns presenting concepts, leading discussions, and facilitating group activities.
- Work together to solve problems, clarify doubts, and reinforce your understanding.
Four other activities
Expand to see all activities and additional details
Show all seven activities
Follow Guided Tutorials on Interpretability and Transparency in AI
Show steps
Expand your understanding of the concepts covered in the course through guided tutorials.
Browse courses on
Explainable AI
Show steps
Show steps
Show steps
- Identify reputable online platforms or resources that offer guided tutorials on interpretability and transparency in AI.
- Choose tutorials that align with the course objectives and your learning goals.
- Follow the tutorials step-by-step, taking notes and experimenting with the provided examples.
- Seek clarification or ask questions in online forums or discussion groups.
Practice Interpreting Real-World AI Applications
Show steps
Develop your skills in interpreting AI models by working on practical examples.
Browse courses on
AI Applications
Show steps
Show steps
Show steps
- Identify real-world AI applications in various domains, such as healthcare, finance, or manufacturing.
- Analyze the AI models used in these applications and their impact on decision-making.
- Evaluate the interpretability and transparency of these models, considering factors such as explainability and fairness.
- Discuss your findings and insights with peers or online communities.
Create a Visualization Tool for Interpreting AI Models
Show steps
Reinforce your understanding by creating a tool that visualizes the behavior and predictions of AI models.
Browse courses on
Model Interpretability
Show steps
Show steps
Show steps
- Choose an AI model and dataset that you are familiar with.
- Identify the key features and patterns in the data that you want to visualize.
- Design and implement a visualization tool using programming languages or software.
- Test and refine your tool to ensure it effectively conveys the interpretability of the AI model.
Contribute to Open-Source Projects on Interpretable AI
Show steps
Gain practical experience and contribute to the advancement of interpretable AI by participating in open-source projects.
Browse courses on
Community Involvement
Show steps
Show steps
Show steps
- Explore open-source repositories on platforms like GitHub or GitLab for projects related to interpretable AI.
- Identify areas where you can contribute based on your skills and interests.
- Familiarize yourself with the project's codebase and documentation.
- Make code contributions, report bugs, or suggest improvements to enhance the interpretability of the AI models.
Career center
Learners who complete Responsible AI for Developers: Interpretability & Transparency - Português Brasileiro will develop knowledge and skills
that may be useful to these careers:
For more career information including salaries, visit:
OpenCourser.com/course/ruakvs/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/ruakvs/responsible
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Level
Intermediate
Via
Coursera
Institution
Google Cloud
Instructor
Google Cloud Training
Session
August 19, 2024
- September 23, 2024
Language
pt-BR
Good to know
Ensina tópicos relevantes para desenvolvedores e engenheiros que precisam de conhecimento em Inteligência Artificial (IA)
Fornece ferramentas e métodos práticos para interpretabilidade e transparência em dados e modelos de IA
Explora a importância da transparência em IA, o que é fundamental para o desenvolvimento ético e responsável de sistemas de IA
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