RRepoGEO

REPOGEO REPORT · LITE

h9-tect/ML-DL_Roadmap.

Default branch main · commit da89ddd4 · scanned 5/31/2026, 2:42:34 AM

GitHub: 601 stars · 76 forks

AI VISIBILITY SCORE
10 /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
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 h9-tect/ML-DL_Roadmap., 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

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

OVERALL DIRECTION
  • highabout#1
    Add a concise repository description

    Why:

    COPY-PASTE FIX
    A comprehensive, structured roadmap outlining the mathematical foundations and resources needed to master Machine Learning and Deep Learning, from linear algebra to advanced topics.
  • mediumlicense#2
    Add a LICENSE file to the repository

    Why:

    COPY-PASTE FIX
    Choose and add a standard open-source license file (e.g., MIT, Apache-2.0, GPL-3.0) to the repository root.

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 h9-tect/ML-DL_Roadmap.
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Mathematics for Machine Learning
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. Mathematics for Machine Learning · recommended 1×
  2. The Deep Learning Book · recommended 1×
  3. Khan Academy · recommended 1×
  4. 3Blue1Brown · recommended 1×
  5. MIT OpenCourseWare · recommended 1×
  • CATEGORY QUERY
    Where can I find a comprehensive roadmap for the mathematical foundations of deep learning?
    you: not recommended
    AI recommended (in order):
    1. Mathematics for Machine Learning
    2. The Deep Learning Book
    3. Khan Academy
    4. 3Blue1Brown
    5. MIT OpenCourseWare
    6. Numerical Recipes

    AI recommended 6 alternatives but never named h9-tect/ML-DL_Roadmap.. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are the essential math topics required to understand advanced deep learning concepts?
    you: not recommended
    AI recommended (in order):
    1. Stochastic Gradient Descent (SGD)
    2. Adam
    3. RMSprop
    4. Adagrad

    AI recommended 4 alternatives but never named h9-tect/ML-DL_Roadmap.. 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 h9-tect/ML-DL_Roadmap.?
    pass
    AI did not name h9-tect/ML-DL_Roadmap. — 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 h9-tect/ML-DL_Roadmap. in production, what risks or prerequisites should they evaluate first?
    pass
    AI did not name h9-tect/ML-DL_Roadmap. — 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 h9-tect/ML-DL_Roadmap. solve, and who is the primary audience?
    pass
    AI did not name h9-tect/ML-DL_Roadmap. — 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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h9-tect/ML-DL_Roadmap. — 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