shunliz/Machine-Learning

• hightopics#1

  Add comprehensive topics to improve categorization

  原因：

  复制粘贴的修复

  复制

  machine-learning, deep-learning, mathematics, statistics, linear-algebra, probability, algorithms, notes, education, tutorial, theoretical-machine-learning, machine-learning-principles

  待办完成忽略
• highlicense#2

  Add a LICENSE file or clarify licensing terms in README

  原因：

  复制粘贴的修复

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  Create a LICENSE file (e.g., CC BY-NC-SA 4.0 for educational content) or add a clear 'License' section to the README stating the terms of use and attribution, especially given the '内容基本都是从互联网上扒来的' statement.

  待办完成忽略
• mediumreadme#3

  Strengthen the README's opening value proposition

  原因：

  当前

  # 机器学习原理
  
  机器学习原理笔记整理. Gitbook地址https://shunliz.gitbooks.io/machine-learning/content/  
  前半部分关注数学基础，机器学习和深度学习的理论部分，详尽的公式推导。  
  后半部分关注工程实践和理论应用部分

  复制粘贴的修复

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  # 机器学习原理：从数学基础到深度学习实践
  
  本仓库提供一套详尽的机器学习与深度学习原理笔记，特别侧重于**数学基础和理论部分的公式推导**。它旨在为学习者提供一个从底层理解算法到工程实践的全面资源，涵盖数学分析、概率论、线性代数、经典机器学习算法及深度学习理论与应用。

  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 detailed explanations of machine learning mathematical foundations and derivations?

  你：未被推荐
  AI 推荐顺序：

  1. The Elements of Statistical Learning: Data Mining, Inference, and Prediction
  2. Pattern Recognition and Machine Learning
  3. Deep Learning
  4. Machine Learning: A Probabilistic Perspective
  5. MIT OpenCourseWare (OCW)
  6. Stanford University's CS229 (Machine Learning) course notes by Andrew Ng

  AI 推荐了 6 个替代方案，却始终没点名 shunliz/Machine-Learning。这就是要补上的差距。

  查看 AI 完整回答
• 品类问题

  What resources explain core machine learning and deep learning theoretical concepts comprehensively?

  你：未被推荐
  AI 推荐顺序：

  1. Deep Learning" by Ian Goodfellow, Yoshua Bengio, and Aaron Courville
  2. The Elements of Statistical Learning: Data Mining, Inference, and Prediction" by Trevor Hastie, Robert Tibshirani, and Jerome Friedman
  3. Pattern Recognition and Machine Learning" by Christopher Bishop
  4. Coursera's "Deep Learning Specialization" by Andrew Ng (deeplearning.ai)
  5. Neural Networks and Deep Learning" by Michael Nielsen
  6. Mathematics for Machine Learning" by Marc Peter Deisenroth, A. Aldo Faisal, and Cheng Soon Ong

  AI 推荐了 6 个替代方案，却始终没点名 shunliz/Machine-Learning。这就是要补上的差距。

  查看 AI 完整回答

• Metadata completeness

  fail

  建议：

• README presence

  pass

• Compared to common alternatives in this category, what is the core differentiator of shunliz/Machine-Learning?

  pass
  ⚠AI 未点名 shunliz/Machine-Learning —— 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 shunliz/Machine-Learning in production, what risks or prerequisites should they evaluate first?

  pass
  ✓AI 明确点名了 shunliz/Machine-Learning

  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 shunliz/Machine-Learning solve, and who is the primary audience?

  pass
  ⚠AI 未点名 shunliz/Machine-Learning —— 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？