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---
title: README
emoji: 💻
colorFrom: green
colorTo: green
sdk: static
pinned: false
---

<div align="center">

Deep Learning 101

**Deep Learning 101, Taiwan’s pioneering and highest deep learning meetup, launched on 2016/11/11 @ 83F, Taipei 101**

AI是一條孤獨且充滿惶恐及未知的旅程,花俏絢麗的收費課程或活動絕非通往成功的捷徑。<br>
衷心感謝當時來自不同單位的AI同好參與者實名分享的寶貴經驗;如欲移除資訊還請告知。<br>
由 [TonTon Huang Ph.D.](https://twman.org) 發起,及其當時任職公司(台灣雪豹科技)無償贊助場地及茶水點心。<br>
Deep Learning 101 創立初衷,是為了普及與分享深度學習及AI領域的尖端知識,深信AI的價值在於解決真實世界的商業問題。

<img src="./images/DeepLearning101.JPG" width="50%" />

---

🌟 快速連結 (Quick Links)

[![YouTube](https://img.shields.io/badge/📺_YouTube_訂閱-FF0000?style=for-the-badge)](https://www.youtube.com/@DeepLearning101)
[![FB](https://img.shields.io/badge/👥_FB_社群-1877F2?style=for-the-badge)](https://www.facebook.com/groups/525579498272187/)
[![Home](https://img.shields.io/badge/🏠_首頁-333333?style=for-the-badge)](https://deep-learning-101.github.io/)
[![GitHub](https://img.shields.io/badge/⭐_GitHub_點星-feb019?style=for-the-badge)](https://github.com/Deep-Learning-101)
[![Web](https://img.shields.io/badge/🌐_官方網站-00b5ad?style=for-the-badge)](https://www.twman.org/DeepLearning101)
[![HF Space](https://img.shields.io/badge/🤗_HF_Space-FFD21E?style=for-the-badge)](https://huggingface.co/DeepLearning101)

🚀 探索 AI 領域 (Tech Hub):

| [![大語言模型](https://img.shields.io/badge/🤖_大語言模型-007bff?style=for-the-badge)](https://deep-learning-101.github.io/Large-Language-Model) | [![自然語言處理](https://img.shields.io/badge/📝_自然語言處理-28a745?style=for-the-badge)](https://deep-learning-101.github.io/Natural-Language-Processing) | [![電腦視覺](https://img.shields.io/badge/👁️_電腦視覺-17a2b8?style=for-the-badge)](https://deep-learning-101.github.io/Computer-Vision) | [![語音處理](https://img.shields.io/badge/🎤_語音處理-6f42c1?style=for-the-badge)](https://deep-learning-101.github.io/Speech-Processing) |
| :---: | :---: | :---: | :---: |
| [🔗 GitHub Papers](https://github.com/Deep-Learning-101/Natural-Language-Processing-Paper?tab=readme-ov-file#llm) | [🔗 GitHub Papers](https://github.com/Deep-Learning-101/Natural-Language-Processing-Paper) | [🔗 GitHub Papers](https://github.com/Deep-Learning-101/Computer-Vision-Paper) | [🔗 GitHub Papers](https://github.com/Deep-Learning-101/Speech-Processing-Paper) |


📚 精選資源導航

**🔥 嚴選 (必讀)**
* [<kbd>策略</kbd> AI新賽局:企業的入門策略指南](/Blog/AIBeginner)
* [<kbd>評測</kbd> 臺灣 LLM 性能評測與在地化分析](/Blog/TW-LLM-Benchmark)
* [<kbd>實戰</kbd> 從零到一:打造高精準度 RAG 系統](/RAG)
* [<kbd>避坑</kbd> 避開 AI Agent 開發陷阱與解決方案](/agent)
* [<kbd>手把手</kbd> Cloudflared 實作內網穿透 (Tunnel)](/Blog/Cloudflared-Tunnel)
* [<kbd>爆火</kbd> OpenClaw(MoltBot/Clawdbot)讓您焦慮嗎?](/Agent/OpenClaw-Moltbot-Clawdbot)

---

<details>
<summary><b>🛠️ 實戰工具 & Agent 框架</b></summary>

* [Dify, Coze, n8n, AutoGen 熱門框架比較](/Blog/Dify-Coze-n8n-AutoGen-LangChain)
* [推論加速:vLLM, Ollama, SGLang](/Blog/vLLM-Ollama-SGLang-LLaMAcpp)
* [Gemini + LangGraph 全端實戰](/gemini-fullstack-langgraph/FinGenAI)
* [基於 AutoGen 的 FinRobot 體驗](/FinRobot/FinRobot-GOOGL)

</details>

<details>
<summary><b>📝 論文快遞</b></summary>

* [Agentic AI:Gemini 3.1 WebMCP 實戰](/Agent/Gemini-3-1_WebMCP_Deep-Think)
* [NVIDIA PersonaPlex 全雙工語音 AI 深度技術分析](/LLM/PersonaPlex)
* [CoT (思维鏈) is not explainability](/Paper/Chain-of-Thought)
* [arXiv: Potemkin Understanding in LLMs](/Paper/2506.21521_Potemkin-Understanding)
* [arXiv: Agentic Reasoning](/Paper/2502.04644_Oxford_Agentic-Reasoning.html)

</details>

<details>
<summary><b>📝 產業趨勢</b></summary>

* [💰 GenAI 在金融產業的應用分析](/Blog/AIFinTech)
* [🤖 2025 趨勢:AI Robot 陪伴型機器人](/Blog/robot)

</details>

<details>
<summary><b>🚧 踩坑指南 & 科普入門</b></summary>

* [🎓 白話文科普 GenAI (硬體/數據)](/GenAI)
* [🛑 LLM 打完收工?硬體升級重要性](/1010LLM)
* [📘 LLM 入門完整指南:原理與應用](/0204LLM)
* [🎨 Diffusion Model 圖像生成解析](/diffusion)
* [💻 訓練微調 VRAM 估算指南](/GPU)
* [🎙️ ASR/TTS 語音開發避坑](/asr-tts)
* [📝 NLP 自然語言開發避坑](/nlp)
* [🐧 Ubuntu 深度學習環境安裝教學](/101)

</details>

<details>
<summary><b>🛡️ AI 資安與 AIxCC 競賽</b></summary>

* [🏆 冠軍團隊:亞特蘭大 (Team-Atlanta) 解析](/cyber/AIxCC-Atlanta)
* [🥈 亞軍團隊:Trail of Bits 解析](/cyber/AIxCC-Buttercup)
* [🐚 Shellphish:用 LLMs 解決 CTF 挑戰](/cyber/AIxCC-shellphish)
* [🛡️ AI 大模型安全護欄綜合報告](/cyber/LLM-Guard)
* [⚔️ LLM 安全攻防策略深度解析](/cyber/LLM-Offense)

</details>

</div>