REPOGEO REPORT · LITE

roboticcam/machine-learning-notes

Default branch master · commit 9c5ccb5a · scanned 5/25/2026, 12:42:57 AM

GitHub: 9,658 stars · 1,769 forks

AI VISIBILITY SCORE

17 /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

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 roboticcam/machine-learning-notes, 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 H1 to specify comprehensive content

Why:

CURRENT

# Live Machine Learning Class:

COPY-PASTE FIX

# Comprehensive Machine Learning, Probabilistic Models, and Deep Learning Notes & Lectures (2000+ Slides)

hightopics#2

Add relevant topics to improve categorization

Why:

COPY-PASTE FIX

machine-learning, deep-learning, probabilistic-models, generative-ai, notes, lectures, education, pytorch, transformer

highlicense#3

Add a LICENSE file to clarify usage rights

Why:

CURRENT

(no LICENSE file detected — the repo has no recognizable license)

COPY-PASTE FIX

Create a LICENSE file in the repository root, for example, using the Creative Commons Attribution 4.0 International License (CC-BY-4.0) for educational content, or a standard open-source license like MIT for code.

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 roboticcam/machine-learning-notes

Avg rank

—

Lower is better. #1 = top recommendation.

Share of voice

Of all named tools, what % are you?

Top rival

Coursera's Deep Learning Specialization by Andrew Ng

Recommended in 1 of 2 queries

COMPETITOR LEADERBOARD

Coursera's Deep Learning Specialization by Andrew Ng · recommended 1×
fast.ai's "Practical Deep Learning for Coders" course · recommended 1×
Stanford CS229: Machine Learning · recommended 1×
Stanford CS230: Deep Learning · recommended 1×
"Deep Learning" book by Ian Goodfellow, Yoshua Bengio, and Aaron Courville · recommended 1×

CATEGORY QUERY
Where can I find comprehensive machine learning theory notes and practical deep learning demos?
you: not recommended
AI recommended (in order):
1. Coursera's Deep Learning Specialization by Andrew Ng
2. fast.ai's "Practical Deep Learning for Coders" course
3. Stanford CS229: Machine Learning
4. Stanford CS230: Deep Learning
5. "Deep Learning" book by Ian Goodfellow, Yoshua Bengio, and Aaron Courville
6. Kaggle Learn
7. PyTorch Tutorials
8. TensorFlow Tutorials
AI recommended 8 alternatives but never named roboticcam/machine-learning-notes. This is the gap to close.
Show full AI answer
CATEGORY QUERY
Seeking advanced explanations of probabilistic models and generative AI with PyTorch examples.
you: not recommended
AI recommended (in order):
1. Probabilistic Machine Learning: Advanced Topics
2. Deep Learning
3. PyTorch-Lightning
4. Generative Deep Learning: Teaching Machines to Paint, Write, Compose, and Play
5. The Annotated Diffusion Model
6. PyTorch Examples GitHub Repository (pytorch/examples)
7. Practical Deep Learning for Coders
AI recommended 7 alternatives but never named roboticcam/machine-learning-notes. 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 roboticcam/machine-learning-notes?
pass
AI did not name roboticcam/machine-learning-notes — 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 roboticcam/machine-learning-notes in production, what risks or prerequisites should they evaluate first?
pass
AI named roboticcam/machine-learning-notes 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 roboticcam/machine-learning-notes solve, and who is the primary audience?
pass
AI did not name roboticcam/machine-learning-notes — 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 roboticcam/machine-learning-notes. It auto-updates whenever the report is rescanned and links back to the latest report — easy public proof that you care about AI discoverability.

Live preview

MARKDOWN (README)

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HTML

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Deep reports10 / month
Brand-free category queries5 vs 2 in Lite
Prioritized action items8 vs 3 in Lite