orico/www.mlcompendium.com

• highreadme#1

  Reposition README to clarify project type

  原因：

  当前

  The README's first content sentence describes what it covers, not what it is.

  复制粘贴的修复

  复制

  Add a clear, concise sentence immediately after the main title (or as the first paragraph) stating its nature, e.g., "This is a comprehensive, open-source knowledge compendium and reference guide for Machine Learning and Deep Learning, covering 500+ topics."

  待办完成忽略
• highlicense#2

  Add a LICENSE file

  原因：

  复制粘贴的修复

  复制

  Create a `LICENSE` file in the repository root containing the text of the MIT License.

  待办完成忽略
• mediumabout#3

  Refine About description for clarity

  原因：

  当前

  The Machine Learning & Deep Learning Compendium was a list of references in my private & single document, which I curated in order to expand my knowledge, it is now an open knowledge-sharing project compiled using Gitbook.

  复制粘贴的修复

  复制

  A comprehensive, open-source knowledge compendium for Machine Learning & Deep Learning, curated from private references and compiled using Gitbook for public knowledge-sharing.

  待办完成忽略

本次扫描解析到的品类 GEO 通道：google/gemini-2.5-flash, deepseek/deepseek-v4-flash

• 品类问题

  Where can I find a comprehensive guide for modern machine learning and deep learning concepts?

  你：未被推荐
  AI 推荐顺序：

  1. Scikit-Learn
  2. Keras
  3. TensorFlow
  4. Coursera
  5. fast.ai
  6. PyTorch

  AI 推荐了 6 个替代方案，却始终没点名 orico/www.mlcompendium.com。这就是要补上的差距。

  查看 AI 完整回答
• 品类问题

  What resources explain data science management, product design, and deep learning techniques?

  你：未被推荐
  AI 推荐顺序：

  1. The AI-Powered Organization: Six Principles for Turning Big Data into Business Value
  2. Data Science for Business: What You Need to Know About Data Mining and Data-Analytic Thinking
  3. Building a Career in Data Science
  4. Harvard Business Review
  5. Designing Data-Intensive Applications
  6. Inspired: How to Create Tech Products Customers Love
  7. Lean Analytics: Use Data to Build a Better Startup Faster
  8. Hooked: How to Build Habit-Forming Products
  9. Don't Make Me Think, Revisited: A Common Sense Approach to Web Usability
  10. Material Design
  11. Human Interface Guidelines
  12. Deep Learning
  13. Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow
  14. Scikit-Learn
  15. Keras
  16. TensorFlow
  17. Deep Learning with Python
  18. Neural Networks and Deep Learning
  19. Deep Learning Specialization
  20. PyTorch

  AI 推荐了 20 个替代方案，却始终没点名 orico/www.mlcompendium.com。这就是要补上的差距。

  查看 AI 完整回答

• Metadata completeness

  warn

  建议：

• README presence

  pass

• Compared to common alternatives in this category, what is the core differentiator of orico/www.mlcompendium.com?

  pass
  ⚠AI 未点名 orico/www.mlcompendium.com —— 很可能在说另一个项目

  AI 的回答可能信誓旦旦却是错的。请按事实核对：技术栈、目标人群、差异化点是不是和你实际的对得上？

• If a team adopts orico/www.mlcompendium.com in production, what risks or prerequisites should they evaluate first?

  pass
  ✓AI 明确点名了 orico/www.mlcompendium.com

  AI 的回答可能信誓旦旦却是错的。请按事实核对：技术栈、目标人群、差异化点是不是和你实际的对得上？

• In one sentence, what problem does the repo orico/www.mlcompendium.com solve, and who is the primary audience?

  pass
  ⚠AI 未点名 orico/www.mlcompendium.com —— 很可能在说另一个项目

  AI 的回答可能信誓旦旦却是错的。请按事实核对：技术栈、目标人群、差异化点是不是和你实际的对得上？