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patrick-llgc/Learning-Deep-Learning
默认分支 master · commit 7fe953be · 扫描时间 2026/6/24 20:17:59
星标 1,265 · Fork 180
下方为分数趋势(含全部就绪扫描;左旧右新,可横向滚动)。表格明细默认折叠,展开后每页 10 条,最新在上。
共 3 条就绪扫描。点击下方按钮展开表格(每页 10 条,可翻页)。
行动计划告诉你下一步要做什么——按影响力排序、可直接复制粘贴的修改。品类可见性是真正的 GEO 测试:当用户向 AI 提一个不带品牌、本应让 patrick-llgc/Learning-Deep-Learning 浮出水面的问题时,AI 是真的推荐了你,还是推荐了你的竞品?客观检查验证 AI 引擎最先权衡的那些元数据信号。自指检查判断 AI 是否还认识你的名字。
行动计划 — 可复制粘贴的修复
3 条由 gemini-2.5-flash 生成、按优先级排序的修改。修完后请把对应条目标记为完成。
- highreadme#1Reposition README opening to clarify unique value and audience
原因:
当前This repository contains my paper reading notes on deep learning and machine learning. It is inspired by Denny Britz and Daniel Takeshi. A minimalistic webpage generated with Github io can be found here.
复制粘贴的修复This repository contains my personal, curated paper reading notes and insights on deep learning and machine learning, with a strong focus on computer vision, autonomous driving, and medical imaging. It's designed for practitioners and researchers seeking a structured learning journey beyond generic courses, inspired by the approaches of Denny Britz and Daniel Takeshi.
- highlicense#2Add a LICENSE file to the repository
原因:
复制粘贴的修复Create a `LICENSE` file in the repository root, choosing an appropriate open-source license (e.g., MIT, Apache-2.0, GPL-3.0) that reflects how you want your notes to be used and shared.
- highhomepage#3Add the GitHub Pages URL to the repository homepage field
原因:
复制粘贴的修复Add the URL for your GitHub Pages site (e.g., `https://patrick-llgc.github.io/Learning-Deep-Learning/`) to the 'Homepage' field in the repository settings.
本次扫描解析到的品类 GEO 通道:google/gemini-2.5-flash, deepseek/deepseek-v4-flash
品类可见性 — 真正的 GEO 测试
向 google/gemini-2.5-flash 提出的不带品牌问题。AI 推荐了你,还是推荐了别人?
各模型使用同一组问题 — 切换标签对比回答与排名。
- Papers With Code · 被推荐 1 次
- Distill.pub · 被推荐 1 次
- Andrej Karpathy's "Neural Networks: Zero to Hero" · 被推荐 1 次
- "Deep Learning" by Goodfellow, Bengio, and Courville · 被推荐 1 次
- Towards Data Science · 被推荐 1 次
- 品类问题Where can I find curated paper reading lists for beginners in deep learning computer vision?你:未被推荐AI 推荐顺序:
- Papers With Code
- Distill.pub
- Andrej Karpathy's "Neural Networks: Zero to Hero"
- "Deep Learning" by Goodfellow, Bengio, and Courville
- Towards Data Science
- arXiv.org
- Stanford CS231n
AI 推荐了 7 个替代方案,却始终没点名 patrick-llgc/Learning-Deep-Learning。这就是要补上的差距。
查看 AI 完整回答
- 品类问题Seeking comprehensive literature reviews on 3D object detection for autonomous driving applications.你:未被推荐AI 推荐顺序:
- 3D Object Detection for Autonomous Driving: A Survey
- A Survey on 3D Object Detection Methods for Autonomous Driving
- Deep Learning for 3D Point Cloud Processing: A Survey
- A Survey on 3D Object Detection from Point Clouds
- Multi-Modal 3D Object Detection for Autonomous Driving: A Survey
AI 推荐了 5 个替代方案,却始终没点名 patrick-llgc/Learning-Deep-Learning。这就是要补上的差距。
查看 AI 完整回答
客观检查
针对 AI 引擎最看重的元数据信号的规则审计。
- Metadata completenesswarn
建议:
- README presencepass
自指检查
当被直接问到你时,AI 是否还知道你的仓库存在?
- Compared to common alternatives in this category, what is the core differentiator of patrick-llgc/Learning-Deep-Learning?passAI 未点名 patrick-llgc/Learning-Deep-Learning —— 很可能在说另一个项目
AI 的回答可能信誓旦旦却是错的。请按事实核对:技术栈、目标人群、差异化点是不是和你实际的对得上?
- If a team adopts patrick-llgc/Learning-Deep-Learning in production, what risks or prerequisites should they evaluate first?passAI 明确点名了 patrick-llgc/Learning-Deep-Learning
AI 的回答可能信誓旦旦却是错的。请按事实核对:技术栈、目标人群、差异化点是不是和你实际的对得上?
- In one sentence, what problem does the repo patrick-llgc/Learning-Deep-Learning solve, and who is the primary audience?passAI 未点名 patrick-llgc/Learning-Deep-Learning —— 很可能在说另一个项目
AI 的回答可能信誓旦旦却是错的。请按事实核对:技术栈、目标人群、差异化点是不是和你实际的对得上?
嵌入你的 GEO 徽章
把这个徽章贴进 patrick-llgc/Learning-Deep-Learning 的 README。每次重新扫描都会自动更新,并跳到最新报告——是「我在乎 AI 可发现性」最简单的公开证明。
[](https://repogeo.com/zh/r/patrick-llgc/Learning-Deep-Learning)<a href="https://repogeo.com/zh/r/patrick-llgc/Learning-Deep-Learning"><img src="https://repogeo.com/badge/patrick-llgc/Learning-Deep-Learning.svg" alt="RepoGEO" /></a>订阅 Pro,解锁深度诊断
patrick-llgc/Learning-Deep-Learning — 轻量扫描仍免费;本卡列出 Pro 相对轻量的深度额度。
- 深度报告每月 10 次
- 无品牌品类查询5,轻量 2
- 优先行动项8,轻量 3