RRepoGEO

REPOGEO REPORT · LITE

loveunk/machine-learning-deep-learning-notes

Default branch master · commit 743389c0 · scanned 5/18/2026, 9:08:06 PM

GitHub: 2,504 stars · 396 forks

Scan history for this repo

Score trend below includes all ready runs (older left, newer right; scroll horizontally if needed). The table is collapsed by default—expand for newest-first rows, 10 per page.

Score trend (left → right: older → newer)

2 ready scans. Expand the table below for newest-first rows (10 per page, paginated).

AI VISIBILITY SCORE
23 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
1 pass · 0 warn · 1 fail
Objective metadata checks
AI knows your name
2 / 3
Direct prompts that named your repo
HOW TO READ THIS REPORT

Action plan is what to do next — copy-pasteable changes prioritized by impact. Category visibility is the real GEO test: when a user asks an AI a brand-free question that should surface loveunk/machine-learning-deep-learning-notes, does the AI actually recommend you — or your competitors? Objective checks verify the metadata signals AI engines weight first. Self-mention check detects whether AI even knows you exist by name.

Action plan — copy-paste fixes

3 prioritized changes generated by gemini-2.5-flash. Mark items done after you ship the fix.

OVERALL DIRECTION
  • highreadme#1
    Reposition README H1 to clarify repo's nature as a knowledge summary

    Why:

    CURRENT
    # 深度学习（DL/ML）学习路径（2025 现代版）
    COPY-PASTE FIX
    # 深度学习（DL/ML）学习路径与知识总结（2025 现代版）
  • hightopics#2
    Add specific topics to improve categorization and recall

    Why:

    COPY-PASTE FIX
    machine-learning, deep-learning, learning-path, curriculum, ai-education, python, pytorch, llm, generative-ai, transformer, rag, ai-agents, data-science-notes, knowledge-summary
  • highlicense#3
    Add a LICENSE file to the repository

    Why:

    COPY-PASTE FIX
    Create a LICENSE file in the repository root. A common choice for educational content is the MIT License, which allows broad reuse.

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

Category visibility — the real GEO test

Brand-free queries asked to google/gemini-2.5-flash. Did AI recommend you, or someone else?

Same questions for every model — switch tabs to compare answers and rankings.

Recall
0 / 2
0% of queries surface loveunk/machine-learning-deep-learning-notes
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Python
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. Python · recommended 1×
  2. numpy/numpy · recommended 1×
  3. pandas-dev/pandas · recommended 1×
  4. Codecademy · recommended 1×
  5. scikit-learn/scikit-learn · recommended 1×
  • CATEGORY QUERY
    What's a fast-track learning path for machine learning and deep learning beginners?
    you: not recommended
    AI recommended (in order):
    1. Python
    2. NumPy (numpy/numpy)
    3. Pandas (pandas-dev/pandas)
    4. Codecademy
    5. Scikit-learn (scikit-learn/scikit-learn)
    6. Kaggle
    7. Keras (keras-team/keras)
    8. TensorFlow (tensorflow/tensorflow)
    9. Coursera
    10. PyTorch (pytorch/pytorch)
    11. Fast.ai (fastai/fastai)
    12. Jupyter Notebooks (jupyter/notebook)
    13. JupyterLab (jupyterlab/jupyterlab)
    14. Google Colaboratory

    AI recommended 14 alternatives but never named loveunk/machine-learning-deep-learning-notes. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Looking for a modern, practical curriculum to learn deep learning and AI agents.
    you: not recommended
    AI recommended (in order):
    1. fast.ai's Practical Deep Learning for Coders (v5)
    2. DeepLearning.AI's Deep Learning Specialization (Coursera)
    3. Hugging Face's 🤗 Transformers Course (huggingface/transformers)
    4. OpenAI's Spinning Up in Deep Reinforcement Learning (openai/spinningup)
    5. MIT 6.S191: Introduction to Deep Learning
    6. Google's Machine Learning Crash Course

    AI recommended 6 alternatives but never named loveunk/machine-learning-deep-learning-notes. This is the gap to close.

    Show full AI answer

Objective checks

Rule-based audits of metadata signals AI engines weight most.

  • Metadata completeness
    fail

    Suggestion:

  • README presence
    pass

Self-mention check

Does AI even know your repo exists when asked about it directly?

  • Compared to common alternatives in this category, what is the core differentiator of loveunk/machine-learning-deep-learning-notes?
    pass
    AI named loveunk/machine-learning-deep-learning-notes explicitly

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

  • If a team adopts loveunk/machine-learning-deep-learning-notes in production, what risks or prerequisites should they evaluate first?
    pass
    AI named loveunk/machine-learning-deep-learning-notes 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 loveunk/machine-learning-deep-learning-notes solve, and who is the primary audience?
    pass
    AI did not name loveunk/machine-learning-deep-learning-notes — 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?

Embed your GEO score

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MARKDOWN (README)
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loveunk/machine-learning-deep-learning-notes — Lite scans stay free; this card itemizes Pro deep limits vs Lite.

  • Deep reports10 / month
  • Brand-free category queries5 vs 2 in Lite
  • Prioritized action items8 vs 3 in Lite