shunliz/Machine-Learning

• hightopics#1

  Add comprehensive topics to improve categorization

  Why:

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

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• highlicense#2

  Add a LICENSE file or clarify licensing terms in README

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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.

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• mediumreadme#3

  Strengthen the README's opening value proposition

  Why:

  CURRENT

  # 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

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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。

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Category GEO backends resolved for this scan: google/gemini-2.5-flash, deepseek/deepseek-v4-flash

• CATEGORY QUERY

  Where can I find detailed explanations of machine learning mathematical foundations and derivations?

  you: not recommended
  AI recommended (in order):

  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 recommended 6 alternatives but never named shunliz/Machine-Learning. This is the gap to close.

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• CATEGORY QUERY

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

  you: not recommended
  AI recommended (in order):

  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 recommended 6 alternatives but never named shunliz/Machine-Learning. This is the gap to close.

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• Metadata completeness

  fail

  Suggestion:

• README presence

  pass

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

  pass
  ⚠AI did not name shunliz/Machine-Learning — likely talking about a different project

  AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

• If a team adopts shunliz/Machine-Learning in production, what risks or prerequisites should they evaluate first?

  pass
  ✓AI named shunliz/Machine-Learning explicitly

  AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

• In one sentence, what problem does the repo shunliz/Machine-Learning solve, and who is the primary audience?

  pass
  ⚠AI did not name shunliz/Machine-Learning — likely talking about a different project

  AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?