RRepoGEO

REPOGEO REPORT · LITE

TingsongYu/PyTorch-Tutorial-2nd

Default branch main · commit 13e8f6e7 · scanned 5/21/2026, 11:07:00 AM

GitHub: 4,522 stars · 484 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
15 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
1 pass · 1 warn · 0 fail
Objective metadata checks
AI knows your name
0 / 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 TingsongYu/PyTorch-Tutorial-2nd, 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
  • highlicense#1
    Add a LICENSE file to the repository root

    Why:

    COPY-PASTE FIX
    Create a `LICENSE` file in the repository root with the MIT License text.
  • highreadme#2
    Clarify the README's opening to emphasize it's a comprehensive tutorial/book

    Why:

    CURRENT
    # PyTorch 实用教程（第二版）
    COPY-PASTE FIX
    # PyTorch 实用教程（第二版） - A Comprehensive Tutorial and Practical Guide
  • mediumtopics#3
    Add more specific topics related to learning and tutorials

    Why:

    CURRENT
    computer-vision, deepsort, diffusion-models, llm, onnx, pytorch, pytorch-tutorial, qwen, tensorrt, yolov5
    COPY-PASTE FIX
    computer-vision, deepsort, diffusion-models, llm, onnx, pytorch, pytorch-tutorial, qwen, tensorrt, yolov5, deep-learning-tutorial, machine-learning-book, deep-learning-course, practical-guide

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 TingsongYu/PyTorch-Tutorial-2nd
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
pytorch/examples
Recommended in 3 of 2 queries
COMPETITOR LEADERBOARD
  1. pytorch/examples · recommended 3×
  2. huggingface/transformers · recommended 2×
  3. fast.ai · recommended 1×
  4. fastai library · recommended 1×
  5. DataCamp · recommended 1×
  • CATEGORY QUERY
    How can I learn PyTorch from scratch for deep learning applications and deployment?
    you: not recommended
    AI recommended (in order):
    1. PyTorch Examples (pytorch/examples)
    2. fast.ai
    3. fastai library
    4. DataCamp
    5. Coursera
    6. PyTorch Geometric
    7. PyTorch Lightning
    8. ONNX
    9. ONNX Runtime
    10. TensorRT
    11. TorchScript
    12. AWS SageMaker
    13. Google Cloud AI Platform
    14. Azure Machine Learning

    AI recommended 14 alternatives but never named TingsongYu/PyTorch-Tutorial-2nd. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Looking for practical PyTorch examples for computer vision, NLP, and large language model projects.
    you: not recommended
    AI recommended (in order):
    1. PyTorch Examples (pytorch/examples)
    2. PyTorch Image Models (timm) (rwightman/pytorch-image-models)
    3. Detectron2 (facebookresearch/detectron2)
    4. MONAI (Project-MONAI/MONAI)
    5. Hugging Face Transformers Library (huggingface/transformers)
    6. PyTorch Examples (pytorch/examples)
    7. AllenNLP (allenai/allennlp)
    8. Hugging Face Transformers Library (huggingface/transformers)
    9. Lit-GPT (Lightning-AI/lit-gpt)
    10. DeepSpeed (microsoft/DeepSpeed)
    11. vLLM (vllm-project/vllm)

    AI recommended 11 alternatives but never named TingsongYu/PyTorch-Tutorial-2nd. This is the gap to close.

    Show full AI answer

Objective checks

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

  • Metadata completeness
    warn

    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 TingsongYu/PyTorch-Tutorial-2nd?
    pass
    AI did not name TingsongYu/PyTorch-Tutorial-2nd — 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 TingsongYu/PyTorch-Tutorial-2nd in production, what risks or prerequisites should they evaluate first?
    pass
    AI did not name TingsongYu/PyTorch-Tutorial-2nd — 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 TingsongYu/PyTorch-Tutorial-2nd solve, and who is the primary audience?
    pass
    AI did not name TingsongYu/PyTorch-Tutorial-2nd — 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?

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TingsongYu/PyTorch-Tutorial-2nd — 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