REPOGEO REPORT · LITE
mbadry1/CS231n-2017-Summary
Default branch master · commit 89042d34 · scanned 6/21/2026, 9:32:56 PM
GitHub: 1,579 stars · 456 forks
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.
2 ready scans. Expand the table below for newest-first rows (10 per page, paginated).
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 mbadry1/CS231n-2017-Summary, 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.
- highreadme#1Reposition README opening to highlight its value as a study resource
Why:
CURRENTAfter watching all the videos of the famous Standford's CS231n course that took place in 2017, i decided to take summary of the whole course to help me to remember and to anyone who would like to know about it.
COPY-PASTE FIXThis repository provides a comprehensive summary and detailed study notes for the Stanford CS231n: Convolutional Neural Networks for Visual Recognition course from 2017, designed to serve as a valuable resource for students and self-learners.
- mediumtopics#2Expand topics to include more specific course and study terms
Why:
CURRENTcs231n, deep-learning, neural-network, notes
COPY-PASTE FIXcs231n, deep-learning, neural-network, notes, computer-vision, machine-learning, study-guide, course-notes, stanford-cs231n
- lowhomepage#3Add a homepage URL linking to the official CS231n course
Why:
COPY-PASTE FIXhttp://cs231n.stanford.edu/2017/
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.
- Deep Learning Book by Ian Goodfellow, Yoshua Bengio, and Aaron Courville · recommended 1×
- Computer Vision: Algorithms and Applications by Richard Szeliski · recommended 1×
- Stanford CS231n: Convolutional Neural Networks for Visual Recognition course notes · recommended 1×
- Neural Networks and Deep Learning by Michael Nielsen · recommended 1×
- Coursera's Deep Learning Specialization by Andrew Ng (deeplearning.ai) · recommended 1×
- CATEGORY QUERYWhere can I find comprehensive summaries for understanding deep learning and computer vision fundamentals?you: not recommendedAI recommended (in order):
- Deep Learning Book by Ian Goodfellow, Yoshua Bengio, and Aaron Courville
- Computer Vision: Algorithms and Applications by Richard Szeliski
- Stanford CS231n: Convolutional Neural Networks for Visual Recognition course notes
- Neural Networks and Deep Learning by Michael Nielsen
- Coursera's Deep Learning Specialization by Andrew Ng (deeplearning.ai)
- Learning OpenCV 4 Computer Vision with Python 3 by Joseph Howse, Joe Minichino, and OpenCV community
AI recommended 6 alternatives but never named mbadry1/CS231n-2017-Summary. This is the gap to close.
Show full AI answer
- CATEGORY QUERYSeeking detailed notes on convolutional neural network architectures and training methodologies.you: not recommendedAI recommended (in order):
- Stanford CS231n
- Deep Learning Book
- Neural Networks and Deep Learning
- fast.ai Practical Deep Learning for Coders
- Papers with Code
- PyTorch
- TensorFlow
AI recommended 7 alternatives but never named mbadry1/CS231n-2017-Summary. This is the gap to close.
Show full AI answer
Objective checks
Rule-based audits of metadata signals AI engines weight most.
- Metadata completenesswarn
Suggestion:
- README presencepass
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 mbadry1/CS231n-2017-Summary?passAI named mbadry1/CS231n-2017-Summary explicitly
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?
- If a team adopts mbadry1/CS231n-2017-Summary in production, what risks or prerequisites should they evaluate first?passAI did not name mbadry1/CS231n-2017-Summary — 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 mbadry1/CS231n-2017-Summary solve, and who is the primary audience?passAI did not name mbadry1/CS231n-2017-Summary — 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 mbadry1/CS231n-2017-Summary. 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[](https://repogeo.com/en/r/mbadry1/CS231n-2017-Summary)<a href="https://repogeo.com/en/r/mbadry1/CS231n-2017-Summary"><img src="https://repogeo.com/badge/mbadry1/CS231n-2017-Summary.svg" alt="RepoGEO" /></a>Subscribe to Pro for deep diagnoses
mbadry1/CS231n-2017-Summary — 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