REPOGEO REPORT · LITE

TingsongYu/PyTorch_Tutorial

Default branch master · commit 38fae4a4 · scanned 5/19/2026, 10:52:54 PM

GitHub: 8,019 stars · 1,750 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

30 /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

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 TingsongYu/PyTorch_Tutorial, 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 the repository

Why:

COPY-PASTE FIX

pytorch, deep-learning, machine-learning, tutorial, education, examples, computer-vision, natural-language-processing, llm, onnx, tensorrt

highlicense#2
Add a LICENSE file to the repository
Why:
COPY-PASTE FIX
```
Create a `LICENSE` file in the root directory with the MIT License text.
```

mediumreadme#3

Reposition the README's opening to clearly state its purpose and value

Why:

CURRENT

# Pytorch模型训练实用教程

📢：《PyTorch实用教程》（第二版）已开源，欢迎阅读：https://tingsongyu.github.io/PyTorch-Tutorial-2nd/

📢：《PyTorch实用教程》（第二版）已开源，欢迎阅读：https://tingsongyu.github.io/PyTorch-Tutorial-2nd/

📢：《PyTorch实用教程》（第二版）已开源，欢迎阅读：https://tingsongyu.github.io/PyTorch-Tutorial-2nd/

第二版新增丰富的**深度学习应用案例**和**推理部署框架**，包括CV、NLP和LLM的十多个实战项目，以及ONNX和TensorRT的教程.

# 1.简介

本代码为教程——《Pytorch模型训练实用教程》中配套代码；<br/>
《Pytorch模型训练实用教程》可通过如下方式获取：<br/>

COPY-PASTE FIX

# PyTorch模型训练实用教程：配套代码与实战案例

本仓库提供《PyTorch模型训练实用教程》的配套代码，旨在帮助学习者通过实际案例掌握PyTorch深度学习。内容涵盖CV、NLP和LLM的实战项目，以及ONNX和TensorRT的推理部署教程。

📢：《PyTorch实用教程》（第二版）已开源，欢迎阅读：https://tingsongyu.github.io/PyTorch-Tutorial-2nd/

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

Avg rank

—

Lower is better. #1 = top recommendation.

Share of voice

Of all named tools, what % are you?

Top rival

Lightning-AI/lightning

Recommended in 1 of 2 queries

COMPETITOR LEADERBOARD

Lightning-AI/lightning · recommended 1×
huggingface/transformers · recommended 1×
microsoft/onnxruntime · recommended 1×
pytorch/serve · recommended 1×
mlflow/mlflow · recommended 1×

CATEGORY QUERY
How to get started with practical PyTorch model training and deployment?
you: not recommended
AI recommended (in order):
1. PyTorch Lightning (Lightning-AI/lightning)
2. Hugging Face Transformers (huggingface/transformers)
3. ONNX Runtime (microsoft/onnxruntime)
4. TorchServe (pytorch/serve)
5. MLflow (mlflow/mlflow)
6. TensorBoard (tensorflow/tensorboard)
7. Docker
AI recommended 7 alternatives but never named TingsongYu/PyTorch_Tutorial. This is the gap to close.
Show full AI answer
CATEGORY QUERY
Where can I find resources for learning PyTorch with real-world deep learning examples?
you: not recommended
AI recommended (in order):
1. PyTorch Examples
2. PyTorch Tutorials
3. Deep Learning with PyTorch: A 60 Minute Blitz
4. PyTorch Lightning Examples
5. Fast.ai's 'Practical Deep Learning for Coders' Course
6. Deep Learning with PyTorch
7. Hugging Face Transformers Examples
AI recommended 7 alternatives but never named TingsongYu/PyTorch_Tutorial. 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 TingsongYu/PyTorch_Tutorial?
pass
AI named TingsongYu/PyTorch_Tutorial explicitly
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 in production, what risks or prerequisites should they evaluate first?
pass
AI named TingsongYu/PyTorch_Tutorial 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 TingsongYu/PyTorch_Tutorial solve, and who is the primary audience?
pass
AI named TingsongYu/PyTorch_Tutorial explicitly
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 — 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