REPOGEO REPORT · LITE

dataflowr/notebooks

Default branch master · commit faa6d1c0 · scanned 5/23/2026, 5:37:27 PM

GitHub: 1,260 stars · 332 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

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 dataflowr/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 H1 and opening paragraph to emphasize its course nature

Why:

CURRENT

# Dataflowr: Deep Learning DIY

Code and notebooks for the deep learning course dataflowr.

COPY-PASTE FIX

# Dataflowr: Hands-on Deep Learning Course Notebooks

This repository provides a curated and structured collection of Jupyter notebooks and code, directly supporting the Dataflowr deep learning courses. It offers practical, runnable examples for students and practitioners to learn and apply deep learning concepts with PyTorch.

mediumabout#2

Expand the repository description with more specific keywords

Why:

CURRENT

code for deep learning courses

COPY-PASTE FIX

Practical, hands-on Jupyter notebooks and code for deep learning courses, covering PyTorch tensors, automatic differentiation, and model finetuning.

lowtopics#3

Add more specific topics to improve categorization

Why:

CURRENT

deep-learning, pytorch, tutorials

COPY-PASTE FIX

deep-learning, pytorch, tutorials, jupyter-notebooks, deep-learning-course

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 dataflowr/notebooks

Avg rank

—

Lower is better. #1 = top recommendation.

Share of voice

Of all named tools, what % are you?

Top rival

fast.ai's Practical Deep Learning for Coders (v5)

Recommended in 1 of 2 queries

COMPETITOR LEADERBOARD

fast.ai's Practical Deep Learning for Coders (v5) · recommended 1×
Deep Learning with PyTorch: A 60 Minute Blitz (Official PyTorch Tutorials) · recommended 1×
PyTorch Geometric (PyG) Tutorials · recommended 1×
PyTorch Lightning Documentation and Examples · recommended 1×
Hugging Face Transformers Tutorials · recommended 1×

CATEGORY QUERY
Where can I find practical deep learning course materials with hands-on PyTorch code examples?
you: not recommended
AI recommended (in order):
1. fast.ai's Practical Deep Learning for Coders (v5)
2. Deep Learning with PyTorch: A 60 Minute Blitz (Official PyTorch Tutorials)
3. PyTorch Geometric (PyG) Tutorials
4. PyTorch Lightning Documentation and Examples
5. Hugging Face Transformers Tutorials
6. DeepLearning.AI PyTorch Courses on Coursera
7. PyTorch Examples GitHub Repository
AI recommended 7 alternatives but never named dataflowr/notebooks. This is the gap to close.
Show full AI answer
CATEGORY QUERY
How to learn PyTorch tensors and automatic differentiation through practical deep learning notebooks?
you: not recommended
AI recommended (in order):
1. PyTorch Official Tutorials
2. Fast.ai's "Practical Deep Learning for Coders"
3. DeepLearning.AI's "Deep Learning Specialization"
4. PyTorch Examples (pytorch/examples)
5. Dive into Deep Learning (d2l-ai/d2l-en)
AI recommended 5 alternatives but never named dataflowr/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
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 dataflowr/notebooks?
pass
AI named dataflowr/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 dataflowr/notebooks in production, what risks or prerequisites should they evaluate first?
pass
AI named dataflowr/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 dataflowr/notebooks solve, and who is the primary audience?
pass
AI named dataflowr/notebooks 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 dataflowr/notebooks. 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)

[![RepoGEO](https://repogeo.com/badge/dataflowr/notebooks.svg)](https://repogeo.com/en/r/dataflowr/notebooks)

HTML

<a href="https://repogeo.com/en/r/dataflowr/notebooks"><img src="https://repogeo.com/badge/dataflowr/notebooks.svg" alt="RepoGEO" /></a>

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dataflowr/notebooks — 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