RRepoGEO

REPOGEO REPORT · LITE

loveunk/machine-learning-deep-learning-notes

Default branch master · commit 61f28950 · scanned 6/30/2026, 3:52:30 AM

GitHub: 2,572 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
22 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
1 pass · 1 warn · 0 fail
Objective metadata checks
AI knows your name
1 / 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
  • hightopics#1
    Add relevant topics to the repository

    Why:

    COPY-PASTE FIX
    ai-learning-path, deep-learning, machine-learning, llm, rag, ai-agent, multimodal-ai, ai-roadmap, python, pytorch
  • highlicense#2
    Add a LICENSE file to the repository

    Why:

    COPY-PASTE FIX
    Create a LICENSE file in the repository root with a standard open-source license (e.g., MIT, Apache-2.0, GPL-3.0) that aligns with the project's intent.
  • mediumabout#3
    Update the repository description to emphasize 'AI learning roadmap'

    Why:

    CURRENT
    机器学习、深度学习的学习路径及知识总结
    COPY-PASTE FIX
    面向中文学习者的 AI 学习路线图：从机器学习、深度学习基础，到大语言模型、RAG、Agent 和多模态工程实践。

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
Pinecone
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. Pinecone · recommended 2×
  2. Weaviate · recommended 2×
  3. Python · recommended 1×
  4. Pandas · recommended 1×
  5. NumPy · recommended 1×
  • CATEGORY QUERY
    What's a good learning path for becoming an AI engineer, covering LLMs and practical deployment?
    you: not recommended
    AI recommended (in order):
    1. Python
    2. Pandas
    3. NumPy
    4. Matplotlib
    5. Seaborn
    6. scikit-learn
    7. PyTorch
    8. TensorFlow
    9. NLTK
    10. spaCy
    11. Hugging Face Transformers (huggingface/transformers)
    12. OpenAI API
    13. Hugging Face Hub
    14. Pinecone
    15. Weaviate
    16. Chroma
    17. LangChain (langchain-ai/langchain)
    18. LlamaIndex (run-llama/llama_index)
    19. AWS SageMaker
    20. Google Cloud Vertex AI
    21. Azure Machine Learning
    22. Docker
    23. FastAPI (tiangolo/fastapi)
    24. Gradio (gradio-app/gradio)
    25. Git
    26. GitHub
    27. Prometheus
    28. Grafana
    29. AWS CloudWatch
    30. Google Cloud Logging

    AI recommended 30 alternatives but never named loveunk/machine-learning-deep-learning-notes. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Guide me through building AI agents and RAG systems from a practical engineering perspective.
    you: not recommended
    AI recommended (in order):
    1. LlamaIndex
    2. LangChain
    3. Haystack (deepset)
    4. OpenAI Assistants API
    5. Weaviate
    6. Pinecone
    7. Faiss (Facebook AI Similarity Search)

    AI recommended 7 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
    warn

    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 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?

  • 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?

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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