RRepoGEO

REPOGEO REPORT · LITE

saurabhaloneai/History-of-Deep-Learning

Default branch main · commit 3bf46e64 · scanned 6/1/2026, 1:17:48 PM

GitHub: 618 stars · 33 forks

AI VISIBILITY SCORE
23 /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
2 / 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 saurabhaloneai/History-of-Deep-Learning, 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
  • hightopics#1
    Add relevant topics to improve categorization

    Why:

    COPY-PASTE FIX
    deep-learning, machine-learning, research-papers, neural-networks, scratch-implementations, paper-implementations, deep-learning-history, ai-education
  • highlicense#2
    Add a LICENSE file to clarify usage rights

    Why:

    COPY-PASTE FIX
    Choose and add a standard open-source license file (e.g., MIT, Apache-2.0) to the repository root.
  • mediumabout#3
    Update the repository description for clarity and keywords

    Why:

    CURRENT
    learningggggggg 🐳
    COPY-PASTE FIX
    A curated collection of important deep learning research papers with scratch implementations, organized by research area to study the evolution of deep learning.

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 saurabhaloneai/History-of-Deep-Learning
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
PyTorch
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. PyTorch · recommended 1×
  2. TensorFlow/Keras · recommended 1×
  3. PyTorch Hub · recommended 1×
  4. TensorFlow Keras Applications · recommended 1×
  5. Hugging Face Transformers library · recommended 1×
  • CATEGORY QUERY
    How can I study the evolution of deep learning models with practical examples?
    you: not recommended
    AI recommended (in order):
    1. PyTorch
    2. TensorFlow/Keras
    3. PyTorch Hub
    4. TensorFlow Keras Applications
    5. Hugging Face Transformers library

    AI recommended 5 alternatives but never named saurabhaloneai/History-of-Deep-Learning. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What resources offer scratch implementations of foundational deep neural network papers?
    you: not recommended
    AI recommended (in order):
    1. Deep Learning from Scratch: Building with Python from First Principles
    2. Neural Networks and Deep Learning
    3. Udacity
    4. fast.ai
    5. GitHub
    6. Towards Data Science
    7. Medium
    8. Analytics Vidhya

    AI recommended 8 alternatives but never named saurabhaloneai/History-of-Deep-Learning. 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 saurabhaloneai/History-of-Deep-Learning?
    pass
    AI named saurabhaloneai/History-of-Deep-Learning explicitly

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

  • If a team adopts saurabhaloneai/History-of-Deep-Learning in production, what risks or prerequisites should they evaluate first?
    pass
    AI named saurabhaloneai/History-of-Deep-Learning 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 saurabhaloneai/History-of-Deep-Learning solve, and who is the primary audience?
    pass
    AI did not name saurabhaloneai/History-of-Deep-Learning — 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 saurabhaloneai/History-of-Deep-Learning. It auto-updates whenever the report is rescanned and links back to the latest report — easy public proof that you care about AI discoverability.

RepoGEO badge previewLive preview
MARKDOWN (README)
[![RepoGEO](https://repogeo.com/badge/saurabhaloneai/History-of-Deep-Learning.svg)](https://repogeo.com/en/r/saurabhaloneai/History-of-Deep-Learning)
HTML
<a href="https://repogeo.com/en/r/saurabhaloneai/History-of-Deep-Learning"><img src="https://repogeo.com/badge/saurabhaloneai/History-of-Deep-Learning.svg" alt="RepoGEO" /></a>
Pro

Subscribe to Pro for deep diagnoses

saurabhaloneai/History-of-Deep-Learning — 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