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이 과정에서는 AI 해석 가능성과 투명성의 개념을 소개합니다. 개발자와 엔지니어에게 AI 투명성이 얼마나 중요한지를 설명합니다. 데이터와 AI 모델 모두에서 해석 가능성과 투명성을 구현하는 데 도움이 되는 실용적인 방법과 도구를 살펴봅니다.
Register for this course and see more details by visiting:
OpenCourser.com/course/ul258t/responsible
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What's inside
Syllabus
과정 소개
이 모듈에서는 과정 구성과 목표를 소개합니다.
AI 해석 가능성 및 투명성
이 모듈에서는 AI 해석 가능성과 투명성을 중점적으로 다룹니다. 데이터와 AI 모델 모두에서 해석 가능성과 투명성을 구현하는 데 도움이 되는 다양한 기법과 도구를 소개합니다.
Read more
Syllabus
과정 소개
이 모듈에서는 과정 구성과 목표를 소개합니다.
AI 해석 가능성 및 투명성
이 모듈에서는 AI 해석 가능성과 투명성을 중점적으로 다룹니다. 데이터와 AI 모델 모두에서 해석 가능성과 투명성을 구현하는 데 도움이 되는 다양한 기법과 도구를 소개합니다.
Read more
과정 요약
이 모듈에서는 가장 중요한 개념, 도구, 기술을 살펴보면서 전체 과정을 요약합니다.
과정 리소스
모든 모듈에 대한 학생 PDF 링크
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
수업 료가 없습니다
공급자는 구글 클라우드 훈련입니다
모든 학습자에게 적합한 코스 난이도입니다
대화형 자료와 실습이 포함됩니다
AI 투명성에 대한 기본 사항을 다룹니다
Save this course
Save Responsible AI for Developers: Interpretability & Transparency - 한국어 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 - 한국어 with these
activities:
Participate in peer study groups or discussion forums on AI interpretability or transparency
Show steps
Engaging with peers will provide different perspectives and facilitate learning.
Show steps
-
Find peer study groups or discussion forums on AI interpretability or transparency
-
Participate in discussions and share insights
-
Collaborate on projects or assignments
Review data visualization concepts
Show steps
Reviewing data visualization concepts will provide a strong foundation for understanding the course materials.
Browse courses on
Data Visualization
Show steps
-
Review books or articles on data visualization
-
Go through online tutorials on data visualization
-
Practice creating data visualizations with a tool like Tableau or Power BI
Attend workshops on AI interpretability or transparency
Show steps
Attending workshops will provide exposure to industry trends and best practices.
Show steps
-
Find workshops on AI interpretability or transparency
-
Attend the workshops
-
Network with other attendees and speakers
Six other activities
Expand to see all activities and additional details
Show all nine activities
Follow tutorials on AI interpretability and transparency techniques
Show steps
Following tutorials will provide hands-on experience with practical techniques for implementing interpretability and transparency in AI models.
Show steps
-
Find tutorials on AI interpretability and transparency
-
Follow the tutorials and implement the techniques on sample datasets
-
Evaluate the results and compare different techniques
Solve practice problems on AI interpretability and transparency
Show steps
Solving practice problems will reinforce the concepts and help students develop problem-solving skills in AI interpretability and transparency.
Show steps
-
Find practice problems on AI interpretability and transparency
-
Solve the problems using the techniques learned in the course
-
Analyze the solutions and learn from mistakes
Contribute to open-source projects related to AI interpretability or transparency
Show steps
Contributing to open-source projects will provide practical experience and connect students with the community.
Show steps
-
Find open-source projects related to AI interpretability or transparency
-
Identify areas where you can contribute
-
Make contributions to the project
-
Engage with the project community
Mentor other students or professionals in AI interpretability or transparency
Show steps
Mentoring others will reinforce your understanding and contribute to the community.
Show steps
-
Identify students or professionals who need mentoring in AI interpretability or transparency
-
Provide guidance and support
-
Share your knowledge and experience
Develop a presentation on an AI interpretability or transparency technique
Show steps
Developing a presentation will provide an opportunity to synthesize and communicate the knowledge gained in the course.
Show steps
-
Choose an AI interpretability or transparency technique
-
Research and gather information on the technique
-
Create a presentation that explains the technique and its applications
-
Present the presentation to a group or online audience
Create a tool or resource for implementing AI interpretability or transparency
Show steps
Creating a tool or resource will allow students to apply their knowledge and contribute to the field.
Show steps
-
Identify a need or problem related to AI interpretability or transparency
-
Design and develop a tool or resource to address the need
-
Test and evaluate the tool or resource
-
Publish or share the tool or resource with the community
Participate in peer study groups or discussion forums on AI interpretability or transparency
Show steps
Engaging with peers will provide different perspectives and facilitate learning.
Show steps
Show steps
Show steps
- Find peer study groups or discussion forums on AI interpretability or transparency
- Participate in discussions and share insights
- Collaborate on projects or assignments
Review data visualization concepts
Show steps
Reviewing data visualization concepts will provide a strong foundation for understanding the course materials.
Browse courses on
Data Visualization
Show steps
Show steps
Show steps
- Review books or articles on data visualization
- Go through online tutorials on data visualization
- Practice creating data visualizations with a tool like Tableau or Power BI
Attend workshops on AI interpretability or transparency
Show steps
Attending workshops will provide exposure to industry trends and best practices.
Show steps
Show steps
Show steps
- Find workshops on AI interpretability or transparency
- Attend the workshops
- Network with other attendees and speakers
Six other activities
Expand to see all activities and additional details
Show all nine activities
Follow tutorials on AI interpretability and transparency techniques
Show steps
Following tutorials will provide hands-on experience with practical techniques for implementing interpretability and transparency in AI models.
Show steps
Show steps
Show steps
- Find tutorials on AI interpretability and transparency
- Follow the tutorials and implement the techniques on sample datasets
- Evaluate the results and compare different techniques
Solve practice problems on AI interpretability and transparency
Show steps
Solving practice problems will reinforce the concepts and help students develop problem-solving skills in AI interpretability and transparency.
Show steps
Show steps
Show steps
- Find practice problems on AI interpretability and transparency
- Solve the problems using the techniques learned in the course
- Analyze the solutions and learn from mistakes
Contribute to open-source projects related to AI interpretability or transparency
Show steps
Contributing to open-source projects will provide practical experience and connect students with the community.
Show steps
Show steps
Show steps
- Find open-source projects related to AI interpretability or transparency
- Identify areas where you can contribute
- Make contributions to the project
- Engage with the project community
Mentor other students or professionals in AI interpretability or transparency
Show steps
Mentoring others will reinforce your understanding and contribute to the community.
Show steps
Show steps
Show steps
- Identify students or professionals who need mentoring in AI interpretability or transparency
- Provide guidance and support
- Share your knowledge and experience
Develop a presentation on an AI interpretability or transparency technique
Show steps
Developing a presentation will provide an opportunity to synthesize and communicate the knowledge gained in the course.
Show steps
Show steps
Show steps
- Choose an AI interpretability or transparency technique
- Research and gather information on the technique
- Create a presentation that explains the technique and its applications
- Present the presentation to a group or online audience
Create a tool or resource for implementing AI interpretability or transparency
Show steps
Creating a tool or resource will allow students to apply their knowledge and contribute to the field.
Show steps
Show steps
Show steps
- Identify a need or problem related to AI interpretability or transparency
- Design and develop a tool or resource to address the need
- Test and evaluate the tool or resource
- Publish or share the tool or resource with the community
Career center
Learners who complete Responsible AI for Developers: Interpretability & Transparency - 한국어 will develop knowledge and skills
that may be useful to these careers:
For more career information including salaries, visit:
OpenCourser.com/course/ul258t/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/ul258t/responsible
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Level
Intermediate
Via
Coursera
Institution
Google Cloud
Instructor
Google Cloud Training
Session
August 19, 2024
- September 23, 2024
Language
한국어
Transcript
English
Good to know
수업 료가 없습니다
공급자는 구글 클라우드 훈련입니다
모든 학습자에게 적합한 코스 난이도입니다
대화형 자료와 실습이 포함됩니다
AI 투명성에 대한 기본 사항을 다룹니다
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