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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.

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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.
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과정 요약
이 모듈에서는 가장 중요한 개념, 도구, 기술을 살펴보면서 전체 과정을 요약합니다.
과정 리소스
모든 모듈에 대한 학생 PDF 링크

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

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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
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
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
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
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Follow tutorials on AI interpretability and transparency techniques
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
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
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
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
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
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

Career center

Learners who complete Responsible AI for Developers: Interpretability & Transparency - 한국어 will develop knowledge and skills that may be useful to these careers:

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