RRepoGEO

REPOGEO REPORT · LITE

neomatrix369/awesome-ai-ml-dl

Default branch master · commit ae332342 · scanned 5/22/2026, 5:47:03 PM

GitHub: 1,668 stars · 372 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
15 /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
0 / 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 neomatrix369/awesome-ai-ml-dl, 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
    Strengthen README opening to emphasize its role as a comprehensive learning resource

    Why:

    CURRENT
    > Learn, build, and explore AI/ML/DL with curated guides, domains, tools, and examples.
    COPY-PASTE FIX
    > A comprehensive, curated list of awesome resources and study notes for learning, building, and exploring Artificial Intelligence, Machine Learning, and Deep Learning.
  • mediumhomepage#2
    Add the repository URL as the homepage in the About section

    Why:

    COPY-PASTE FIX
    https://github.com/neomatrix369/awesome-ai-ml-dl
  • lowreadme#3
    Clarify the project's license directly in the README

    Why:

    COPY-PASTE FIX
    Add a section or line in the README, for example: 'This project is licensed under the terms specified in the [LICENSE](LICENSE) file, which includes elements of [Creative Commons Attribution-ShareAlike 4.0 International License](https://creativecommons.org/licenses/by-sa/4.0/).'

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 neomatrix369/awesome-ai-ml-dl
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Awesome Machine Learning
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. Awesome Machine Learning · recommended 1×
  2. Google's Machine Learning Crash Course · recommended 1×
  3. fast.ai's Practical Deep Learning for Coders · recommended 1×
  4. Kaggle Learn · recommended 1×
  5. Towards Data Science · recommended 1×
  • CATEGORY QUERY
    Where can I find a comprehensive curated list of resources for learning AI and ML?
    you: not recommended
    AI recommended (in order):
    1. Awesome Machine Learning
    2. Google's Machine Learning Crash Course
    3. fast.ai's Practical Deep Learning for Coders
    4. Kaggle Learn
    5. Towards Data Science
    6. OpenAI's Spinning Up in Deep RL

    AI recommended 6 alternatives but never named neomatrix369/awesome-ai-ml-dl. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are the essential tools and infrastructure for developing machine learning applications, including JVM options?
    you: not recommended
    AI recommended (in order):
    1. TensorFlow (tensorflow/tensorflow)
    2. PyTorch (pytorch/pytorch)
    3. Scikit-learn (scikit-learn/scikit-learn)
    4. Pandas (pandas-dev/pandas)
    5. NumPy (numpy/numpy)
    6. Apache Spark MLlib (apache/spark)
    7. Deeplearning4j (eclipse/deeplearning4j)
    8. H2O.ai (h2oai/h2o-3)
    9. PostgreSQL
    10. Apache Kafka (apache/kafka)
    11. MinIO (minio/minio)
    12. Apache Parquet (apache/parquet-format)
    13. MLflow (mlflow/mlflow)
    14. Kubernetes (kubernetes/kubernetes)
    15. Docker (docker/cli)
    16. Amazon Web Services
    17. Google Cloud Platform
    18. Microsoft Azure
    19. Jupyter (jupyter/jupyter)
    20. VS Code (microsoft/vscode)
    21. Git (git/git)
    22. GitHub
    23. GitLab (gitlabhq/gitlabhq)
    24. Bitbucket

    AI recommended 24 alternatives but never named neomatrix369/awesome-ai-ml-dl. 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 neomatrix369/awesome-ai-ml-dl?
    pass
    AI did not name neomatrix369/awesome-ai-ml-dl — 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 neomatrix369/awesome-ai-ml-dl in production, what risks or prerequisites should they evaluate first?
    pass
    AI did not name neomatrix369/awesome-ai-ml-dl — 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 neomatrix369/awesome-ai-ml-dl solve, and who is the primary audience?
    pass
    AI did not name neomatrix369/awesome-ai-ml-dl — 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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neomatrix369/awesome-ai-ml-dl — 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