RRepoGEO

REPOGEO REPORT · LITE

KeKe-Li/tutorial

Default branch master · commit d6d1e40c · scanned 5/10/2026, 10:53:04 PM

GitHub: 845 stars · 199 forks

AI VISIBILITY SCORE
40 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
2 pass · 0 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 KeKe-Li/tutorial, 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's opening to clarify unique value

    Why:

    CURRENT
    ### Deeplearning Algorithms tutorial
    COPY-PASTE FIX
    ### Deeplearning Algorithms Tutorial: A Curated Collection of Practical, From-Scratch Implementations

This repository provides a personally curated collection of practical tutorials and code examples designed to help learners understand and implement fundamental machine learning and deep learning algorithms from scratch.
  • mediumhomepage#2
    Update homepage URL to repository root

    Why:

    CURRENT
    https://github.com/KeKe-Li/tutorial/tree/master
    COPY-PASTE FIX
    https://github.com/KeKe-Li/tutorial
  • lowtopics#3
    Add more specific topics to align with "from scratch" and "practical guides"

    Why:

    CURRENT
    algorithms-tutorial, deeplearning, machine-learning-algorithms, neural-network, tutorial
    COPY-PASTE FIX
    algorithms-tutorial, deeplearning, machine-learning-algorithms, neural-network, tutorial, practical-examples, learn-from-scratch, code-examples

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 KeKe-Li/tutorial
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
scikit-learn/scikit-learn
Recommended in 3 of 2 queries
COMPETITOR LEADERBOARD
  1. scikit-learn/scikit-learn · recommended 3×
  2. fastai/fastai · recommended 1×
  3. DeepLearning.AI's Deep Learning Specialization · recommended 1×
  4. pytorch/pytorch · recommended 1×
  5. 3Blue1Brown's Neural Networks Series · recommended 1×
  • CATEGORY QUERY
    Where can I find comprehensive tutorials explaining deep learning algorithms from scratch?
    you: not recommended
    AI recommended (in order):
    1. fast.ai's Practical Deep Learning for Coders (fastai/fastai)
    2. DeepLearning.AI's Deep Learning Specialization
    3. PyTorch Tutorials (pytorch/pytorch)
    4. 3Blue1Brown's Neural Networks Series
    5. Stanford CS231n: Convolutional Neural Networks for Visual Recognition
    6. "Deep Learning" by Ian Goodfellow, Yoshua Bengio, and Aaron Courville

    AI recommended 6 alternatives but never named KeKe-Li/tutorial. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Looking for practical guides to understand and implement fundamental machine learning algorithms.
    you: not recommended
    AI recommended (in order):
    1. Scikit-Learn (scikit-learn/scikit-learn)
    2. Keras (keras-team/keras)
    3. TensorFlow (tensorflow/tensorflow)
    4. NumPy (numpy/numpy)
    5. SciPy (scipy/scipy)
    6. Coursera
    7. Octave (gnu-octave/octave)
    8. MATLAB
    9. Scikit-learn (scikit-learn/scikit-learn)
    10. Scikit-learn (scikit-learn/scikit-learn)

    AI recommended 10 alternatives but never named KeKe-Li/tutorial. This is the gap to close.

    Show full AI answer

Objective checks

Rule-based audits of metadata signals AI engines weight most.

  • Metadata completeness
    pass

  • 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 KeKe-Li/tutorial?
    pass
    AI named KeKe-Li/tutorial explicitly

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

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

    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 KeKe-Li/tutorial. It auto-updates whenever the report is rescanned and links back to the latest report — easy public proof that you care about AI discoverability.

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MARKDOWN (README)
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HTML
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KeKe-Li/tutorial — 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