RRepoGEO

REPOGEO REPORT · LITE

dair-ai/ML-Notebooks

Default branch main · commit 2dbc350c · scanned 5/11/2026, 3:03:23 PM

GitHub: 3,445 stars · 538 forks

AI VISIBILITY SCORE
35 /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
3 / 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 dair-ai/ML-Notebooks, 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 the README's opening paragraph to emphasize its purpose as a curated notebook collection

    Why:

    CURRENT
    This repo contains machine learning notebooks for different tasks and applications. The notebooks are meant to be minimal, easily reusable, and extendable. You are free to use them for educational and research purposes.
    COPY-PASTE FIX
    This repository offers a curated collection of practical, runnable Jupyter notebooks designed to demonstrate and explain core machine learning and deep learning concepts. Ideal for students, beginners, and practitioners, these notebooks provide easy-to-use Python examples for quick experimentation and educational purposes.
  • mediumabout#2
    Add a homepage URL to the repository's 'About' section

    Why:

    COPY-PASTE FIX
    https://github.com/dair-ai/ML-Notebooks
  • lowtopics#3
    Add more specific topics to improve categorization as an educational notebook collection

    Why:

    CURRENT
    ai, deep-learning, machine-learning, python, pytorch
    COPY-PASTE FIX
    ai, deep-learning, machine-learning, python, pytorch, jupyter-notebooks, tutorials, examples, learning

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 dair-ai/ML-Notebooks
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Kaggle
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. Kaggle · recommended 1×
  2. PyTorch · recommended 1×
  3. TensorFlow · recommended 1×
  4. fast.ai · recommended 1×
  5. Awesome Deep Learning · recommended 1×
  • CATEGORY QUERY
    Where can I find practical machine learning notebooks for deep learning tasks in Python?
    you: not recommended
    AI recommended (in order):
    1. Kaggle
    2. PyTorch
    3. TensorFlow
    4. fast.ai
    5. Awesome Deep Learning

    AI recommended 5 alternatives but never named dair-ai/ML-Notebooks. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Seeking easy-to-use Python deep learning examples with minimal setup for quick experimentation.
    you: not recommended
    AI recommended (in order):
    1. Keras
    2. PyTorch Lightning
    3. fastai
    4. Hugging Face Transformers
    5. scikit-learn

    AI recommended 5 alternatives but never named dair-ai/ML-Notebooks. 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 dair-ai/ML-Notebooks?
    pass
    AI named dair-ai/ML-Notebooks explicitly

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

  • If a team adopts dair-ai/ML-Notebooks in production, what risks or prerequisites should they evaluate first?
    pass
    AI named dair-ai/ML-Notebooks 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 dair-ai/ML-Notebooks solve, and who is the primary audience?
    pass
    AI named dair-ai/ML-Notebooks explicitly

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

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