RRepoGEO

REPOGEO REPORT · LITE

orico/www.mlcompendium.com

Default branch main · commit 4c787070 · scanned 5/18/2026, 11:43:18 AM

GitHub: 2,187 stars · 235 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 orico/www.mlcompendium.com, 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 to clarify project type

    Why:

    CURRENT
    The README's first content sentence describes what it covers, not what it is.
    COPY-PASTE FIX
    Add a clear, concise sentence immediately after the main title (or as the first paragraph) stating its nature, e.g., "This is a comprehensive, open-source knowledge compendium and reference guide for Machine Learning and Deep Learning, covering 500+ topics."
  • highlicense#2
    Add a LICENSE file

    Why:

    COPY-PASTE FIX
    Create a `LICENSE` file in the repository root containing the text of the MIT License.
  • mediumabout#3
    Refine About description for clarity

    Why:

    CURRENT
    The Machine Learning & Deep Learning Compendium was a list of references in my private & single document, which I curated in order to expand my knowledge, it is now an open knowledge-sharing project compiled using Gitbook.
    COPY-PASTE FIX
    A comprehensive, open-source knowledge compendium for Machine Learning & Deep Learning, curated from private references and compiled using Gitbook for public knowledge-sharing.

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 orico/www.mlcompendium.com
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Scikit-Learn
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. Scikit-Learn · recommended 2×
  2. Keras · recommended 2×
  3. TensorFlow · recommended 2×
  4. PyTorch · recommended 2×
  5. Coursera · recommended 1×
  • CATEGORY QUERY
    Where can I find a comprehensive guide for modern machine learning and deep learning concepts?
    you: not recommended
    AI recommended (in order):
    1. Scikit-Learn
    2. Keras
    3. TensorFlow
    4. Coursera
    5. fast.ai
    6. PyTorch

    AI recommended 6 alternatives but never named orico/www.mlcompendium.com. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What resources explain data science management, product design, and deep learning techniques?
    you: not recommended
    AI recommended (in order):
    1. The AI-Powered Organization: Six Principles for Turning Big Data into Business Value
    2. Data Science for Business: What You Need to Know About Data Mining and Data-Analytic Thinking
    3. Building a Career in Data Science
    4. Harvard Business Review
    5. Designing Data-Intensive Applications
    6. Inspired: How to Create Tech Products Customers Love
    7. Lean Analytics: Use Data to Build a Better Startup Faster
    8. Hooked: How to Build Habit-Forming Products
    9. Don't Make Me Think, Revisited: A Common Sense Approach to Web Usability
    10. Material Design
    11. Human Interface Guidelines
    12. Deep Learning
    13. Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow
    14. Scikit-Learn
    15. Keras
    16. TensorFlow
    17. Deep Learning with Python
    18. Neural Networks and Deep Learning
    19. Deep Learning Specialization
    20. PyTorch

    AI recommended 20 alternatives but never named orico/www.mlcompendium.com. 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 orico/www.mlcompendium.com?
    pass
    AI did not name orico/www.mlcompendium.com — 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 orico/www.mlcompendium.com in production, what risks or prerequisites should they evaluate first?
    pass
    AI named orico/www.mlcompendium.com 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 orico/www.mlcompendium.com solve, and who is the primary audience?
    pass
    AI did not name orico/www.mlcompendium.com — 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

Drop this badge into the README of orico/www.mlcompendium.com. It auto-updates whenever the report is rescanned and links back to the latest report — easy public proof that you care about AI discoverability.

RepoGEO badge previewLive preview
MARKDOWN (README)
[![RepoGEO](https://repogeo.com/badge/orico/www.mlcompendium.com.svg)](https://repogeo.com/en/r/orico/www.mlcompendium.com)
HTML
<a href="https://repogeo.com/en/r/orico/www.mlcompendium.com"><img src="https://repogeo.com/badge/orico/www.mlcompendium.com.svg" alt="RepoGEO" /></a>
Pro

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orico/www.mlcompendium.com — Lite scans stay free; this card itemizes Pro deep limits vs Lite.

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  • Brand-free category queries5 vs 2 in Lite
  • Prioritized action items8 vs 3 in Lite
orico/www.mlcompendium.com — RepoGEO report