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Responsible AI for Developers

Interpretability & Transparency - 繁體中文

Google Cloud Training

本課程旨在說明 AI 的可解釋性和透明度概念、探討 AI 透明度對開發人員和工程師的重要性。課程中也會介紹實務方法和工具，有助於讓資料和 AI 模型透明且可解釋。

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What's inside

Syllabus

課程簡介
本單元介紹課程架構和目標。
AI 可解釋性和透明度
本單元著重介紹 AI 的可解釋性和透明度，並提供多種技術與工具，有助於讓資料和 AI 模型透明且可解釋。
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課程摘要
本單元介紹最重要的概念、工具和技術，概略說明整個課程內容。
課程資源
連結至所有單元的學員用 PDF

Good to know

Know what's good
, what to watch for
, and possible dealbreakers
適合想要學習 AI 可解釋性和透明度基礎知識的學習者。
有助於開發人員和工程師讓資料和 AI 模型更易於理解。
授課講師為 Google Cloud Training，在相關領域具有專業知識。

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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 - 繁體中文 with these activities:
Review Statistics Concepts
Refreshing statistics knowledge helps students better understand the statistical models and data analysis techniques used in AI.
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  • Review basic statistical concepts, such as mean, median, and standard deviation
  • Practice applying statistical methods to real-world AI datasets
Connect with AI Professionals
Connecting with professionals in the AI field provides valuable insights, career advice, and potential mentorship opportunities.
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Show steps
  • Attend industry events or conferences
  • Reach out to AI professionals on LinkedIn
  • Seek guidance from professors or teaching assistants
Explore Interactive AI Demonstrations
Interactive demonstrations provide visual and practical insights into AI concepts, making them more accessible and engaging.
Browse courses on AI Applications
Show steps
  • Search for online AI demonstrations or tutorials
  • Choose a demonstration that relates to the course topics
  • Follow the instructions and explore the interactive elements
Four other activities
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Summarize AI Techniques
Creating summaries of AI techniques covered in this course helps improve memorization and understanding of the topic.
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  • Review notes and materials from the course
  • Summarize each technique in a few sentences, highlighting its purpose and key features
  • Create a document or presentation with your summaries
Solve AI Algorithm Problems
Solving AI algorithm problems enhances critical thinking, problem-solving skills, and deepens understanding of AI techniques.
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Show steps
  • Find online platforms or textbooks with AI algorithm problems
  • Choose problems that relate to the course topics
  • Implement the algorithms and test their performance
Practice Explaining AI Terms
Explaining AI concepts to peers reinforces understanding and strengthens communication skills.
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Show steps
  • Form a study group with other students
  • Choose an AI term or concept to present
  • Prepare a brief presentation explaining the term
  • Take turns presenting and explaining to each other
Design an AI Transparency Framework
Creating an AI transparency framework helps students delve into the ethical implications of AI and develop critical thinking skills.
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  • Research ethical guidelines and best practices for AI transparency
  • Identify key stakeholders and their concerns
  • Design a framework that outlines transparency measures, data collection, and communication strategies
  • Present your framework to the class for feedback

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

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Course image
Level
Intermediate
Via
Coursera
Institution
Google Cloud
Instructor
Google Cloud Training
Session
August 19, 2024 - September 23, 2024
Language
zh-TW
Good to know
適合想要學習 AI 可解釋性和透明度基礎知識的學習者。
有助於開發人員和工程師讓資料和 AI 模型更易於理解。
授課講師為 Google Cloud Training，在相關領域具有專業知識。
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