# Ilya Sutskever推荐30u30论文精读

**Repository Path**: Zen07/IlyaSutskever-30u30-AI-Paper-Notes

## Basic Information

- **Project Name**: Ilya Sutskever推荐30u30论文精读
- **Description**: Ilya Sutskever 推荐的论文清单：30u30。
Ilya Sutskever 是 Hinton 的学生，OpenAI的联合创始人。
以下是他推荐的论文清单，他认为阅读完这些内容之后就可以了解AI领域90%的内容
- **Primary Language**: TeX/LaTeX
- **License**: MIT
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 7
- **Forks**: 0
- **Created**: 2025-02-26
- **Last Updated**: 2025-09-27

## Categories & Tags

**Categories**: Uncategorized

**Tags**: 论文学习

## README

# Ilya Sutskever推荐30u30论文精读

[![LICENSE](https://img.shields.io/badge/license-MIT-blue.svg)](LICENSE)
[![论文进度](https://img.shields.io/badge/论文进度-27/27-orange.svg)]()
[![贡献者](https://img.shields.io/badge/贡献者-欢迎加入-brightgreen.svg)]()

## 项目介绍

本仓库基于OpenAI联合创始人兼前首席科学家Ilya Sutskever推荐的27篇顶尖AI论文，旨在系统化学习和记录AI领域的核心论文。这些论文涵盖了深度学习的基础架构、应用创新和理论突破，Sutskever认为通过学习这些内容可掌握AI领域约90%的核心知识。

### 项目目标
- 提供每篇论文的中文解读和关键概念提取
- 整理实用代码示例和实现思路
- 建立从基础到前沿的AI学习路径
- 形成社区协作的开放学习环境

## 论文清单与学习状态

### 💡 核心神经网络创新
| 状态 | 论文名称 | 核心贡献 | 学习资源 |
|------|---------|---------|---------|
| ✅ | **Recurrent Neural Network Regularization** | 提出dropout等正则化技术显著提升LSTM性能 | [论文解读](https://gitee.com/Zen07/IlyaSutskever-30u30-AI-Paper-Notes/blob/master/0-Core-Innovations/Recurrent%20Neural%20Network%20Regularization/%E8%AE%BA%E6%96%87%E8%A7%A3%E8%AF%BB%EF%BC%9A%E3%80%8A%E9%80%92%E5%BD%92%E7%A5%9E%E7%BB%8F%E7%BD%91%E7%BB%9C%E6%AD%A3%E5%88%99%E5%8C%96%E3%80%8B.pdf) |
| ✅ | **Pointer Networks** | 创新性解决输出空间大小可变的序列生成问题 | [论文解读1](https://gitee.com/Zen07/IlyaSutskever-30u30-AI-Paper-Notes/blob/master/0-Core-Innovations/Pointer%20Networks/Pointer%20Networks%E7%AE%80%E4%BB%8B%E5%8F%8A%E5%85%B6%E5%BA%94%E7%94%A8.pdf), [论文解读2](https://gitee.com/Zen07/IlyaSutskever-30u30-AI-Paper-Notes/blob/master/0-Core-Innovations/Pointer%20Networks/%E7%BB%8F%E5%85%B8%E8%AE%BA%E6%96%87%E5%9B%9E%E9%A1%BE%E2%80%94%E2%80%94Pointer%20Networks.pdf) |
| ✅ | **Deep Residual Learning for Image Recognition** | 通过残差连接解决深层网络训练难题 | [论文翻译](https://gitee.com/Zen07/IlyaSutskever-30u30-AI-Paper-Notes/blob/master/0-Core-Innovations/Deep%20Residual%20Learning%20for%20Image%20Recognition/Deep%20Residual%20Learning%20for%20Image%20Recognition%E4%B8%AD%E8%8B%B1%E5%AF%B9%E7%85%A7%E5%8F%8C%E8%AF%AD%E7%89%88.pdf), [论文解读1](https://gitee.com/Zen07/IlyaSutskever-30u30-AI-Paper-Notes/blob/master/0-Core-Innovations/Deep%20Residual%20Learning%20for%20Image%20Recognition/ResNet%EF%BC%88%E6%AE%8B%E5%B7%AE%E7%BD%91%E7%BB%9C%EF%BC%89%E5%87%A0%E4%B8%AA%E5%85%B3%E9%94%AE%E9%97%AE%E9%A2%98%E7%9A%84%E7%90%86%E8%A7%A3_%E6%AE%8B%E5%B7%AE%E7%BD%91%E7%BB%9C%20%E6%81%92%E7%AD%89%E6%98%A0%E5%B0%84.pdf), [论文解读2](https://gitee.com/Zen07/IlyaSutskever-30u30-AI-Paper-Notes/blob/master/0-Core-Innovations/Deep%20Residual%20Learning%20for%20Image%20Recognition/%E8%AE%BA%E6%96%87%E7%B2%BE%E8%AF%BB%E2%80%94%E2%80%94ResNet%20%EF%BC%88Deep%20Residual%20Learning%20for%20Image%20Recognition%EF%BC%89%E6%AE%8B%E5%B7%AE%E7%BD%91%E7%BB%9C_resnet%E8%AE%BA%E6%96%87.pdf) |
| ✅ | **Identity Mappings in Deep Residual Networks** | 改进残差网络设计，提升性能和训练稳定性 | [论文翻译](https://gitee.com/Zen07/IlyaSutskever-30u30-AI-Paper-Notes/blob/master/0-Core-Innovations/Identity%20Mappings%20in%20Deep%20Residual%20Networks/Identity%20Mappings%20in%20Deep%20Residual%20Networks%E4%B8%AD%E8%8B%B1%E5%AF%B9%E7%85%A7%E7%89%88.pdf), [论文解读](https://gitee.com/Zen07/IlyaSutskever-30u30-AI-Paper-Notes/blob/master/0-Core-Innovations/Identity%20Mappings%20in%20Deep%20Residual%20Networks/%E7%A7%92%E6%87%82%EF%BC%81%E4%BD%95%E5%87%AF%E6%98%8E%E7%9A%84%E6%B7%B1%E5%BA%A6%E6%AE%8B%E5%B7%AE%E7%BD%91%E7%BB%9CPPT%E6%98%AF%E8%BF%99%E6%A0%B7%E7%9A%84_ICML2016%20tutorial.pdf) |
| ✅ | **Neural Turing Machines** | 结合神经网络与外部存储，增强算法能力 | [论文翻译](https://gitee.com/Zen07/IlyaSutskever-30u30-AI-Paper-Notes/blob/master/0-Core-Innovations/Neural%20Turing%20Machines/Neural%20Machine%20Translation%20by%20Jointly%20Learning%20to%20Align%20and%20Translate%E4%B8%AD%E8%8B%B1%E5%8F%8C%E8%AF%AD%E5%AF%B9%E7%85%A7%E7%89%88.pdf), [论文解读1](https://gitee.com/Zen07/IlyaSutskever-30u30-AI-Paper-Notes/blob/master/0-Core-Innovations/Neural%20Turing%20Machines/Neural%20Turing%20Machine%20-%20%E7%A5%9E%E7%BB%8F%E5%9B%BE%E7%81%B5%E6%9C%BA.pdf), [论文解读2](https://gitee.com/Zen07/IlyaSutskever-30u30-AI-Paper-Notes/blob/master/0-Core-Innovations/Neural%20Turing%20Machines/%E7%A5%9E%E7%BB%8F%E7%BD%91%E7%BB%9C%E5%9B%BE%E7%81%B5%E6%9C%BA%E7%9A%84%E9%80%9A%E4%BF%97%E8%A7%A3%E9%87%8A%E5%92%8C%E8%AF%A6%E7%BB%86%E8%BF%87%E7%A8%8B%E5%8F%8A%E5%BA%94%E7%94%A8%EF%BC%9F.pdf) |
| ✅ | **Attention Is All You Need** | 提出Transformer架构，彻底改变NLP领域 | [论文翻译](https://gitee.com/Zen07/IlyaSutskever-30u30-AI-Paper-Notes/blob/master/0-Core-Innovations/Attention%20is%20All%20You%20Need/Attention%20is%20all%20you%20need%E4%B8%AD%E8%8B%B1%E5%AF%B9%E7%85%A7%E7%89%88.pdf), [论文解读](https://gitee.com/Zen07/IlyaSutskever-30u30-AI-Paper-Notes/blob/master/0-Core-Innovations/Attention%20is%20All%20You%20Need/%E6%9D%8E%E6%B2%90%E7%B2%BE%E8%AF%BB%E8%AE%BA%E6%96%87%EF%BC%9Atransformer%20%E3%80%8AAttention%20Is%20All%20You%20Need%E3%80%8B%20by%20Google_attention%20is%20all%20you%20need.pdf), [论文解读视频](https://www.bilibili.com/video/BV1pu411o7BE/?share_source=copy_web&vd_source=ce8447c351cf8b99b86437a6a7708262) |

### 🔬 专业神经网络应用
| 状态 | 论文名称 | 核心贡献 | 学习资源 |
|------|---------|---------|---------|
| ✅ | **Multi-Scale Context Aggregation by Dilated Convolutions** | 提出扩张卷积改进语义分割效果 | [论文翻译](https://gitee.com/Zen07/IlyaSutskever-30u30-AI-Paper-Notes/blob/master/1-Applied-NNs/Multi-Scale%20Context%20Aggregation%20by%20Dilated%20Convolutions/Multi-Scale%20Context%20Aggregation%20by%20Dilated%20Convolutions%E4%B8%AD%E8%8B%B1%E5%AF%B9%E7%85%A7%E7%89%88.pdf), [论文解读1](https://gitee.com/Zen07/IlyaSutskever-30u30-AI-Paper-Notes/blob/master/1-Applied-NNs/Multi-Scale%20Context%20Aggregation%20by%20Dilated%20Convolutions/%E3%80%90%E8%AF%AD%E4%B9%89%E5%88%86%E5%89%B2%E4%B8%93%E9%A2%98%E3%80%91%E8%AF%AD%E4%B9%89%E5%88%86%E5%89%B2%E7%9B%B8%E5%85%B3%E5%B7%A5%E4%BD%9C--Multi-Scale%20Context%20Aggregation%20by%20Dilated%20Convolution.pdf), [论文解读2](https://gitee.com/Zen07/IlyaSutskever-30u30-AI-Paper-Notes/blob/master/1-Applied-NNs/Multi-Scale%20Context%20Aggregation%20by%20Dilated%20Convolutions/%E5%AF%B9Dilated%20Convolution%E7%90%86%E8%A7%A3.pdf) |
| ✅ | **Neural Machine Translation by Jointly Learning to Align and Translate** | 引入注意力机制提升机器翻译质量 | [论文翻译](https://gitee.com/Zen07/IlyaSutskever-30u30-AI-Paper-Notes/blob/master/1-Applied-NNs/Neural%20Machine%20Translation%20by%20Jointly%20Learning%20to%20Align%20and%20Translate/Neural%20Machine%20Translation%20by%20Jointly%20Learning%20to%20Align%20and%20Translate%E4%B8%AD%E8%8B%B1%E5%8F%8C%E8%AF%AD%E5%AF%B9%E7%85%A7%E7%89%88.pdf), [论文解读](https://gitee.com/Zen07/IlyaSutskever-30u30-AI-Paper-Notes/blob/master/1-Applied-NNs/Neural%20Machine%20Translation%20by%20Jointly%20Learning%20to%20Align%20and%20Translate/%E4%B8%80%E6%96%87%E8%AF%BB%E6%87%82%E7%A5%9E%E7%BB%8F%E6%9C%BA%E5%99%A8%E7%BF%BB%E8%AF%91%E7%9A%84%E5%AF%B9%E9%BD%90%E4%B8%8E%E7%BF%BB%E8%AF%91%E8%81%94%E5%90%88%E5%AD%A6%E4%B9%A0.pdf) |
| ✅ | **Neural Message Passing for Quantum Chemistry** | 创新分子图学习框架，应用于量子化学 | [论文翻译](https://gitee.com/Zen07/IlyaSutskever-30u30-AI-Paper-Notes/blob/master/1-Applied-NNs/Neural%20Message%20Passing%20for%20Quantum%20Chemistry/Neural%20Message%20Passing%20for%20Quantum%20Chemistry%E4%B8%AD%E8%8B%B1%E5%AF%B9%E7%85%A7%E7%89%88.pdf), [论文解读](https://gitee.com/Zen07/IlyaSutskever-30u30-AI-Paper-Notes/blob/master/1-Applied-NNs/Neural%20Message%20Passing%20for%20Quantum%20Chemistry/%E8%AE%BA%E6%96%87%E7%AC%94%E8%AE%B0%EF%BC%9ANeural%20Message%20Passing%20for%20Quantum%20Chemistry_neu-%20ral%20message%20passing%20for%20quantum%20chemistry.pdf) |
| ✅ | **Relational RNNs** | 增强记忆架构的关系推理能力 | [论文解读](https://gitee.com/Zen07/IlyaSutskever-30u30-AI-Paper-Notes/blob/master/1-Applied-NNs/Relational%20recurrent%20neural%20networks/DeepMind%E6%8F%90%E5%87%BA%E5%85%B3%E7%B3%BBRNN%EF%BC%9A%E6%9E%84%E5%BB%BA%E5%85%B3%E7%B3%BB%E6%8E%A8%E7%90%86%E6%A8%A1%E5%9D%97%EF%BC%8C%E5%BC%BA%E5%8C%96%E5%AD%A6%E4%B9%A0%E5%88%A9%E5%99%A8.pdf) |
| ✅ | **Deep Speech 2: End-to-End Speech Recognition in English and Mandarin** | 端到端深度学习语音识别系统 | [论文翻译](https://gitee.com/Zen07/IlyaSutskever-30u30-AI-Paper-Notes/blob/master/1-Applied-NNs/Deep%20Speech%202%EF%BC%9AEnd-to-End%20Speech%20Recognition%20in%20English%20and%20Mandarin/%E3%80%90%E8%AE%BA%E6%96%87%E7%BF%BB%E8%AF%91%E3%80%91Deep%20Speech%202%EF%BC%88%E7%99%BE%E5%BA%A6,%202015%EF%BC%89%20_%20End-to-End%20Speech%20Recognition%20in%20English%20and%20Mandarin.pdf) |
| ✅ | **ImageNet Classification with Deep CNNs** | 开创性CNN架构，奠定计算机视觉基础 | [论文翻译](https://gitee.com/Zen07/IlyaSutskever-30u30-AI-Paper-Notes/blob/master/1-Applied-NNs/ImageNet%20Classification%20with%20Deep%20Convolutional%20Neural%20Networks/ImageNet%20classification%20with%20deep%20convolutional%20neural%20networks%E4%B8%AD%E8%8B%B1%E5%8F%8C%E8%AF%AD%E5%AF%B9%E7%85%A7%E7%89%88.pdf), [论文解读](https://gitee.com/Zen07/IlyaSutskever-30u30-AI-Paper-Notes/blob/master/1-Applied-NNs/ImageNet%20Classification%20with%20Deep%20Convolutional%20Neural%20Networks/ImageNet%20Classification%20with%20Deep%EF%BC%88PPT%E6%95%99%E6%A1%88%EF%BC%89.pdf), [论文解读视频](https://www.bilibili.com/video/BV1hq4y157t1/?share_source=copy_web&vd_source=ce8447c351cf8b99b86437a6a7708262) |
| ✅ | **Variational Lossy Autoencoder** | 结合VAE与自回归模型改进图像生成 | [论文翻译](https://gitee.com/Zen07/IlyaSutskever-30u30-AI-Paper-Notes/blob/master/1-Applied-NNs/Variational%20Lossy%20Autoencoder/Variational%20Lossy%20Autoencoder%E4%B8%AD%E8%8B%B1%E5%AF%B9%E7%85%A7%E7%89%88.pdf), [论文解读](https://gitee.com/Zen07/IlyaSutskever-30u30-AI-Paper-Notes/blob/master/1-Applied-NNs/Variational%20Lossy%20Autoencoder/%E8%AE%BA%E6%96%87%E9%98%85%E8%AF%BB__%E7%9F%A5%E8%AF%86%E7%82%B9-%E6%89%A9%E6%95%A3%E6%A8%A1%E5%9E%8B3-Variational%20Lossy%20Autoencoder.pdf) |
| ✅ | **A Simple NN Module for Relational Reasoning** | 设计专用关系推理神经网络模块 | [论文翻译](https://gitee.com/Zen07/IlyaSutskever-30u30-AI-Paper-Notes/blob/master/1-Applied-NNs/A%20Simple%20NN%20Module%20for%20Relational%20Reasoning/A%20simple%20neural%20network%20module%20for%20relational%20reasoning%E4%B8%AD%E8%8B%B1%E5%AF%B9%E7%85%A7%E7%89%88.pdf) |

### 🧠 理论见解和原则性方法
| 状态 | 论文名称 | 核心贡献 | 学习资源 |
|------|---------|---------|---------|
| ✅ | **Order Matters: Sequence to sequence for sets** | 研究数据顺序对模型性能的影响 | [论文翻译](https://gitee.com/Zen07/IlyaSutskever-30u30-AI-Paper-Notes/blob/master/2-Theoretical-Insights/Order%20Matters%EF%BC%9ASequence%20to%20sequence%20for%20sets/Order%20Matters_%20Sequence%20to%20sequence%20for%20sets%E4%B8%AD%E8%8B%B1%E5%AF%B9%E7%85%A7%E7%89%88.pdf)  |
| ✅ | **Scaling Laws for Neural LMs** | 揭示语言模型性能与规模的数学关系 | [论文解读](https://gitee.com/Zen07/IlyaSutskever-30u30-AI-Paper-Notes/blob/master/2-Theoretical-Insights/Scaling%20Laws%20for%20Neural%20Language%20Models/OpenAI%E7%9A%84Scaling%20Law%E8%AE%BA%E6%96%87%E7%AC%94%E8%AE%B0-CSDN%E5%8D%9A%E5%AE%A2.pdf) |
| ✅ | **A Tutorial Introduction to the Minimum Description Length Principle** | MDL原理在模型选择中的应用教程 | [论文解读](https://gitee.com/Zen07/IlyaSutskever-30u30-AI-Paper-Notes/blob/master/2-Theoretical-Insights/A%20Tutorial%20Introduction%20to%20the%20Minimum%20Description%20Length%20Principle/%E6%9C%80%E5%B0%8F%E6%8F%8F%E8%BF%B0%E9%95%BF%E5%BA%A6%E5%8E%9F%E7%90%86%20-%20%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0%E7%9A%84%E5%9F%BA%E7%A1%80.pdf) |
| ✅ | **Keeping Neural Networks Simple by Minimizing the Description Length of the Weights** | 通过最小化权重描述长度提高泛化能力 | [论文解读](https://gitee.com/Zen07/IlyaSutskever-30u30-AI-Paper-Notes/blob/master/2-Theoretical-Insights/Keeping%20Neural%20Networks%20Simple%20by%20Minimizing%20the%20Description%20Length%20of%20the%20Weights/Keeping%20Neural%20Networks%20Simple%20by%20Minimizing%20the%20Description%20Length%20of%20the%20Weights%20_%20Dotneteers.net.pdf) |
| ✅ | **Machine Super Intelligence Dissertation** | 研究智能体在可计算环境中的最优行为 | [论文解读](https://gitee.com/Zen07/IlyaSutskever-30u30-AI-Paper-Notes/blob/master/2-Theoretical-Insights/%09Machine%20Super%20Intelligence%20Dissertation/Shane%20Legg%20-%20Machine%20Super%20Intelligence%20(2008)%20_%20tomrochette.com.pdf) |
| ✅ | **Kolmogorov Complexity (PAGE 434 onwards)** | 探索信息论与计算复杂性的数学基础 | [论文解读](https://gitee.com/Zen07/IlyaSutskever-30u30-AI-Paper-Notes/blob/master/2-Theoretical-Insights/Kolmogorov%20Complexity%20(PAGE%20434%20onwards)/%E7%AE%97%E6%B3%95%E4%BF%A1%E6%81%AF%E8%AE%BA%5B1%5D%EF%BC%9A%E6%9F%AF%E5%B0%94%E8%8E%AB%E5%93%A5%E6%B4%9B%E5%A4%AB%E5%A4%8D%E6%9D%82%E5%BA%A6.pdf) |

### 🔄 跨学科和概念研究
| 状态 | 论文名称 | 核心贡献 | 学习资源 |
|------|---------|---------|---------|
| ✅ | **Quantifying the Rise and Fall of Complexity in Closed Systems: The Coffee Automaton** | 使用元胞自动机研究封闭系统复杂性演化 | [论文解读](https://gitee.com/Zen07/IlyaSutskever-30u30-AI-Paper-Notes/blob/master/3-Interdisciplinary/Quantifying%20the%20Rise%20and%20Fall%20of%20Complexity%20in%20Closed%20Systems:%20The%20Coffee%20Automaton/Shtetl-Optimized%20%20%E5%8D%9A%E5%AE%A2%E5%AD%98%E6%A1%A3%20%20%E9%87%8F%E5%8C%96%E5%B0%81%E9%97%AD%E7%B3%BB%E7%BB%9F%E4%B8%AD%E5%A4%8D%E6%9D%82%E6%80%A7%E7%9A%84%E5%85%B4%E8%A1%B0%EF%BC%9A%E5%92%96%E5%95%A1%E8%87%AA%E5%8A%A8%E6%9C%BA%20---%20Shtetl-Optimized%20%20Blog%20Archive%20%20Quantifying%20the%20Rise%20and%20Fall%20of%20Complexity%20in%20Closed%20Systems_%20The%20Coffee%20Automaton.pdf) |

### ⚡ 效率和可扩展性技术
| 状态 | 论文名称 | 核心贡献 | 学习资源 |
|------|---------|---------|---------|
| ✅ | **GPipe: Efficient Training of Giant Neural Networks using Pipeline Parallelism** | 提出流水线并行训练大规模神经网络方法 | [论文解读](https://gitee.com/Zen07/IlyaSutskever-30u30-AI-Paper-Notes/blob/master/4-Efficiency&Scalability-Technologies/GPipe:%20Efficient%20Training%20of%20Giant%20Neural%20Networksusing%20Pipeline%20Parallelism/%E8%AE%BA%E6%96%87%E7%AC%94%E8%AE%B0%20--%20GPipe%20Efficient%20Training%20of%20Giant%20Neural%20Networks%20using%20Pipeline%20Parallelism%20%E2%80%94%20%E7%8E%9B%E4%B8%BD%E8%8E%B2%E6%A2%A6%E5%A5%87.pdf) |

### 📖 教材和教程
| 状态 | 论文名称 | 核心贡献 | 学习资源 |
|------|---------|---------|---------|
| ✅ | **CS231n: Convolutional Neural Networks for Visual Recognition** | 斯坦福经典CNN视觉识别课程 | [官方笔记授权翻译](https://zhuanlan.zhihu.com/p/21930884) |
| ✅ | **The Annotated Transformer** | Transformer论文的详细注释实现 | [论文解读](https://gitee.com/Zen07/IlyaSutskever-30u30-AI-Paper-Notes/blob/master/5-Tutorials/The%20Annotated%20Transformer/The%20Annotated%20Transformer%E7%9A%84%E4%B8%AD%E6%96%87%E6%B3%A8%E9%87%8A%E7%89%88%EF%BC%881%EF%BC%89.pdf) |
| ✅ | **The First Law of Complexodynamics** | 计算系统复杂性度量的理论探讨 | [论文解读](https://gitee.com/Zen07/IlyaSutskever-30u30-AI-Paper-Notes/blob/master/5-Tutorials/The%20First%20Law%20of%20Complexodynamics/%E8%A7%A3%E7%A0%81%E5%A4%8D%E6%9D%82%E5%8A%A8%E5%8A%9B%E5%AD%A6%EF%BC%9A%E4%BB%8E%E6%96%AF%E7%A7%91%E7%89%B9%C2%B7%E9%98%BF%E4%BC%A6%E6%A3%AE%E7%9A%84%E7%AC%AC%E4%B8%80%E5%AE%9A%E5%BE%8B%E4%B8%AD%E8%8E%B7%E5%BE%97%E8%A7%81%E8%A7%A3%20_%20by%20Sy%20_%20Medium%20---%20Decoding%20Complexodynamics_%20Insights%20from%20Scott%20Aaronson%E2%80%99s%20First%20Law%20_%20by%20Sy%20_%20Medium.pdf) |
| ✅ | **The Unreasonable Effectiveness of RNNs** | 展示RNN在多种任务中的惊人能力 | [论文解读](https://gitee.com/Zen07/IlyaSutskever-30u30-AI-Paper-Notes/blob/master/5-Tutorials/The%20Unreasonable%20Effectiveness%20of%20Recurrent%20Neural%20Networks/RNN%E7%9A%84%E7%A5%9E%E5%A5%87%E4%B9%8B%E5%A4%84%EF%BC%88The%20Unreasonable%20Effectiveness%20of%20Recurrent%20Neural%20Networks%EF%BC%89.pdf) |
| ✅ | **Understanding LSTM Networks** | LSTM网络工作原理的清晰解释 | [论文解读1](https://gitee.com/Zen07/IlyaSutskever-30u30-AI-Paper-Notes/blob/master/5-Tutorials/Understanding%20LSTM%20Networks/%E4%BA%86%E8%A7%A3LSTM%E7%BD%91%E7%BB%9C%EF%BC%88%E8%8B%B1%E6%96%87%E5%8D%9A%E5%AE%A2%E6%B1%89%E5%8C%96%EF%BC%89.pdf), [论文解读2](https://gitee.com/Zen07/IlyaSutskever-30u30-AI-Paper-Notes/blob/master/5-Tutorials/Understanding%20LSTM%20Networks/DL%E4%B9%8BLSTM%EF%BC%9A%E3%80%8AUnderstanding%20LSTM%20Networks%E4%BA%86%E8%A7%A3LSTM%E7%BD%91%E7%BB%9C%E3%80%8B%E7%9A%84%E7%BF%BB%E8%AF%91%E5%B9%B6%E8%A7%A3%E8%AF%BB-CSDN%E5%8D%9A%E5%AE%A2.pdf) |

## 📚 推荐学习路径

### 入门阶段 (1-2个月)
1. **基础理论打牢**: 
   - 先学习《Understanding LSTM Networks》和《The Unreasonable Effectiveness of RNNs》
   - 完成Stanford CS231n课程中的CNN基础部分
   - 理解神经网络基本架构和训练原理

2. **核心模型理解** (2-3个月):
   - 学习《Attention Is All You Need》掌握Transformer架构
   - 研究ResNet系列论文了解深度网络设计
   - 探索LSTM及其变体的工作机制

3. **高级主题探索** (3+个月):
   - 深入《Scaling Laws for Neural LMs》理解大模型规律
   - 学习MDL原理和信息论基础
   - 研究最新模型优化和训练技术

每篇论文建议学习周期：
- 精读论文：2-3天
- 理解代码实现：3-5天
- 实践与实验：1-2周

## 📊 项目进度

### 总体完成情况
- 论文阅读进度: [==============================] 27/27 (100%)
- 代码实现示例: [=__________________________] 1/27 (0%)
- 中文详解笔记: [______________________________] 0/27 (0%)

### 近期完成论文
| 日期 | 论文 | 完成度 | 资源链接 |
|------|------|--------|----------|
| 2025-03-14 | CS231n: Convolutional Neural Networks for Visual Recognition | 100% | [官方笔记授权翻译](https://zhuanlan.zhihu.com/p/21930884) |
| 2025-03-14 | The First Law of Complexodynamics ![The First Law of Complexodynamics](https://gitee.com/Zen07/IlyaSutskever-30u30-AI-Paper-Notes/raw/master/resources/imgs/The%20First%20Law%20of%20Complexodynamics.png) | 100% | [论文解读](https://gitee.com/Zen07/IlyaSutskever-30u30-AI-Paper-Notes/blob/master/5-Tutorials/The%20First%20Law%20of%20Complexodynamics/%E8%A7%A3%E7%A0%81%E5%A4%8D%E6%9D%82%E5%8A%A8%E5%8A%9B%E5%AD%A6%EF%BC%9A%E4%BB%8E%E6%96%AF%E7%A7%91%E7%89%B9%C2%B7%E9%98%BF%E4%BC%A6%E6%A3%AE%E7%9A%84%E7%AC%AC%E4%B8%80%E5%AE%9A%E5%BE%8B%E4%B8%AD%E8%8E%B7%E5%BE%97%E8%A7%81%E8%A7%A3%20_%20by%20Sy%20_%20Medium%20---%20Decoding%20Complexodynamics_%20Insights%20from%20Scott%20Aaronson%E2%80%99s%20First%20Law%20_%20by%20Sy%20_%20Medium.pdf) |
| 2025-03-14 | The Annotated Transformer ![The Annotated Transformer](https://gitee.com/Zen07/IlyaSutskever-30u30-AI-Paper-Notes/raw/master/resources/imgs/The%20Annotated%20Transformer.png) | 100% | [论文解读](https://gitee.com/Zen07/IlyaSutskever-30u30-AI-Paper-Notes/blob/master/5-Tutorials/The%20Annotated%20Transformer/The%20Annotated%20Transformer%E7%9A%84%E4%B8%AD%E6%96%87%E6%B3%A8%E9%87%8A%E7%89%88%EF%BC%881%EF%BC%89.pdf) |
| 2025-03-13 | GPipe: Efficient Training of Giant Neural Networks using Pipeline Parallelism ![GPipe: Efficient Training of Giant Neural Networks using Pipeline Parallelism](https://gitee.com/Zen07/IlyaSutskever-30u30-AI-Paper-Notes/raw/master/resources/imgs/GPipe%EF%BC%9AEfficient%20Training%20of%20Giant%20Neural%20Networks%20using%20Pipeline%20Parallelism.png) | 100% | [论文解读](https://gitee.com/Zen07/IlyaSutskever-30u30-AI-Paper-Notes/blob/master/4-Efficiency&Scalability-Technologies/GPipe:%20Efficient%20Training%20of%20Giant%20Neural%20Networksusing%20Pipeline%20Parallelism/%E8%AE%BA%E6%96%87%E7%AC%94%E8%AE%B0%20--%20GPipe%20Efficient%20Training%20of%20Giant%20Neural%20Networks%20using%20Pipeline%20Parallelism%20%E2%80%94%20%E7%8E%9B%E4%B8%BD%E8%8E%B2%E6%A2%A6%E5%A5%87.pdf) |
| 2025-03-13 | Quantifying the Rise and Fall of Complexity in Closed Systems: The Coffee Automaton ![Quantifying the Rise and Fall of Complexity in Closed Systems: The Coffee Automaton](https://gitee.com/Zen07/IlyaSutskever-30u30-AI-Paper-Notes/raw/master/resources/imgs/Quantifying%20the%20Rise%20and%20Fall%20of%20Complexity%20in%20Closed%20Systems%EF%BC%9AThe%20Coffee%20Automaton.png) | 100% | [论文解读](https://gitee.com/Zen07/IlyaSutskever-30u30-AI-Paper-Notes/blob/master/3-Interdisciplinary/Quantifying%20the%20Rise%20and%20Fall%20of%20Complexity%20in%20Closed%20Systems:%20The%20Coffee%20Automaton/Shtetl-Optimized%20%20%E5%8D%9A%E5%AE%A2%E5%AD%98%E6%A1%A3%20%20%E9%87%8F%E5%8C%96%E5%B0%81%E9%97%AD%E7%B3%BB%E7%BB%9F%E4%B8%AD%E5%A4%8D%E6%9D%82%E6%80%A7%E7%9A%84%E5%85%B4%E8%A1%B0%EF%BC%9A%E5%92%96%E5%95%A1%E8%87%AA%E5%8A%A8%E6%9C%BA%20---%20Shtetl-Optimized%20%20Blog%20Archive%20%20Quantifying%20the%20Rise%20and%20Fall%20of%20Complexity%20in%20Closed%20Systems_%20The%20Coffee%20Automaton.pdf) |
| 2025-03-13 | Kolmogorov Complexity (PAGE 434 onwards) ![Kolmogorov Complexity (PAGE 434 onwards)](https://gitee.com/Zen07/IlyaSutskever-30u30-AI-Paper-Notes/raw/master/resources/imgs/Komogrov%20Complexity.png) | 100% | [论文解读](https://gitee.com/Zen07/IlyaSutskever-30u30-AI-Paper-Notes/blob/master/2-Theoretical-Insights/Kolmogorov%20Complexity%20(PAGE%20434%20onwards)/%E7%AE%97%E6%B3%95%E4%BF%A1%E6%81%AF%E8%AE%BA%5B1%5D%EF%BC%9A%E6%9F%AF%E5%B0%94%E8%8E%AB%E5%93%A5%E6%B4%9B%E5%A4%AB%E5%A4%8D%E6%9D%82%E5%BA%A6.pdf) |
| 2025-03-13 | Machine Super Intelligence Dissertation ![Machine Super Intelligence Dissertation](https://gitee.com/Zen07/IlyaSutskever-30u30-AI-Paper-Notes/raw/master/resources/imgs/Machine%20Super%20Intelligence%20Dissertation.png) | 100% | [论文解读](https://gitee.com/Zen07/IlyaSutskever-30u30-AI-Paper-Notes/blob/master/2-Theoretical-Insights/%09Machine%20Super%20Intelligence%20Dissertation/Shane%20Legg%20-%20Machine%20Super%20Intelligence%20(2008)%20_%20tomrochette.com.pdf) |
| 2025-03-13 | Keeping Neural Networks Simple by Minimizing the Description Length of the Weights ![Keeping Neural Networks Simple by Minimizing the Description Length of the Weights](https://gitee.com/Zen07/IlyaSutskever-30u30-AI-Paper-Notes/raw/master/resources/imgs/Keeping%20Neural%20Networks%20Simple%20by%20Minimizing%20the%20Description%20Length%20of%20the%20Weights.png) | 100% | [论文解读](https://gitee.com/Zen07/IlyaSutskever-30u30-AI-Paper-Notes/blob/master/2-Theoretical-Insights/Keeping%20Neural%20Networks%20Simple%20by%20Minimizing%20the%20Description%20Length%20of%20the%20Weights/Keeping%20Neural%20Networks%20Simple%20by%20Minimizing%20the%20Description%20Length%20of%20the%20Weights%20_%20Dotneteers.net.pdf) |
| 2025-03-12 | A Tutorial Introduction to the Minimum Description Length Principle ![A Tutorial Introduction to the Minimum Description Length Principle](https://gitee.com/Zen07/IlyaSutskever-30u30-AI-Paper-Notes/raw/master/resources/imgs/A%20Tutorial%20Introduction%20to%20the%20Minimum%20Description%20Length%20Principle.png) | 100% | [论文解读](https://gitee.com/Zen07/IlyaSutskever-30u30-AI-Paper-Notes/blob/master/2-Theoretical-Insights/A%20Tutorial%20Introduction%20to%20the%20Minimum%20Description%20Length%20Principle/%E6%9C%80%E5%B0%8F%E6%8F%8F%E8%BF%B0%E9%95%BF%E5%BA%A6%E5%8E%9F%E7%90%86%20-%20%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0%E7%9A%84%E5%9F%BA%E7%A1%80.pdf) |
| 2025-03-12 | Scaling Laws for Neural LMs ![Scaling Laws for Neural LMs](https://gitee.com/Zen07/IlyaSutskever-30u30-AI-Paper-Notes/raw/master/resources/imgs/Scaling%20Laws%20for%20Neural%20Language%20Models.png) | 100% | [论文解读](https://gitee.com/Zen07/IlyaSutskever-30u30-AI-Paper-Notes/blob/master/2-Theoretical-Insights/Scaling%20Laws%20for%20Neural%20Language%20Models/OpenAI%E7%9A%84Scaling%20Law%E8%AE%BA%E6%96%87%E7%AC%94%E8%AE%B0-CSDN%E5%8D%9A%E5%AE%A2.pdf) |
| 2025-03-11 | Order Matters: Sequence to sequence for sets ![Order Matters: Sequence to sequence for sets](https://gitee.com/Zen07/IlyaSutskever-30u30-AI-Paper-Notes/raw/master/resources/imgs/Order%20Matters%EF%BC%9ASequence%20to%20sequence%20for%20sets.png) | 100% | [论文翻译](https://gitee.com/Zen07/IlyaSutskever-30u30-AI-Paper-Notes/blob/master/2-Theoretical-Insights/Order%20Matters%EF%BC%9ASequence%20to%20sequence%20for%20sets/Order%20Matters_%20Sequence%20to%20sequence%20for%20sets%E4%B8%AD%E8%8B%B1%E5%AF%B9%E7%85%A7%E7%89%88.pdf) |
| 2025-03-11 | Attention Is All You Need ![Attention Is All You Need](https://gitee.com/Zen07/IlyaSutskever-30u30-AI-Paper-Notes/raw/master/resources/imgs/Attention%20Is%20All%20You%20Need.png) | 100% | [论文翻译](https://gitee.com/Zen07/IlyaSutskever-30u30-AI-Paper-Notes/blob/master/0-Core-Innovations/Attention%20is%20All%20You%20Need/Attention%20is%20all%20you%20need%E4%B8%AD%E8%8B%B1%E5%AF%B9%E7%85%A7%E7%89%88.pdf), [论文解读](https://gitee.com/Zen07/IlyaSutskever-30u30-AI-Paper-Notes/blob/master/0-Core-Innovations/Attention%20is%20All%20You%20Need/%E6%9D%8E%E6%B2%90%E7%B2%BE%E8%AF%BB%E8%AE%BA%E6%96%87%EF%BC%9Atransformer%20%E3%80%8AAttention%20Is%20All%20You%20Need%E3%80%8B%20by%20Google_attention%20is%20all%20you%20need.pdf), [论文解读视频](https://www.bilibili.com/video/BV1pu411o7BE/?share_source=copy_web&vd_source=ce8447c351cf8b99b86437a6a7708262) |
| 2025-03-10 | A Simple NN Module for Relational Reasoning ![A Simple NN Module for Relational Reasoning](https://gitee.com/Zen07/IlyaSutskever-30u30-AI-Paper-Notes/raw/master/resources/imgs/A%20simple%20neural%20network%20module%20for%20relational%20reasoning.png) | 100% | [论文翻译](https://gitee.com/Zen07/IlyaSutskever-30u30-AI-Paper-Notes/blob/master/1-Applied-NNs/A%20Simple%20NN%20Module%20for%20Relational%20Reasoning/A%20simple%20neural%20network%20module%20for%20relational%20reasoning%E4%B8%AD%E8%8B%B1%E5%AF%B9%E7%85%A7%E7%89%88.pdf) |
| 2025-03-10 | Variational Lossy Autoencoder ![Variational Lossy Autoencoder](https://gitee.com/Zen07/IlyaSutskever-30u30-AI-Paper-Notes/raw/master/resources/imgs/Variational%20Lossy%20Autoencoder.png) | 100% | [论文翻译](https://gitee.com/Zen07/IlyaSutskever-30u30-AI-Paper-Notes/blob/master/1-Applied-NNs/Variational%20Lossy%20Autoencoder/Variational%20Lossy%20Autoencoder%E4%B8%AD%E8%8B%B1%E5%AF%B9%E7%85%A7%E7%89%88.pdf), [论文解读](https://gitee.com/Zen07/IlyaSutskever-30u30-AI-Paper-Notes/blob/master/1-Applied-NNs/Variational%20Lossy%20Autoencoder/%E8%AE%BA%E6%96%87%E9%98%85%E8%AF%BB__%E7%9F%A5%E8%AF%86%E7%82%B9-%E6%89%A9%E6%95%A3%E6%A8%A1%E5%9E%8B3-Variational%20Lossy%20Autoencoder.pdf) |
| 2025-03-09 | Deep Speech 2: End-to-End Speech Recognition in English and Mandarin ![Deep Speech 2: End-to-End Speech Recognition in English and Mandarin](https://gitee.com/Zen07/IlyaSutskever-30u30-AI-Paper-Notes/raw/master/resources/imgs/Deep%20Speech%EF%BC%9AEnd-to-End%20Speech%20Recognition%20in%20English%20and%20Mandarin.png) | 100% | [论文翻译](https://gitee.com/Zen07/IlyaSutskever-30u30-AI-Paper-Notes/blob/master/1-Applied-NNs/Deep%20Speech%202%EF%BC%9AEnd-to-End%20Speech%20Recognition%20in%20English%20and%20Mandarin/%E3%80%90%E8%AE%BA%E6%96%87%E7%BF%BB%E8%AF%91%E3%80%91Deep%20Speech%202%EF%BC%88%E7%99%BE%E5%BA%A6,%202015%EF%BC%89%20_%20End-to-End%20Speech%20Recognition%20in%20English%20and%20Mandarin.pdf) |
| 2025-03-09 | Neural Message Passing for Quantum Chemistry ![Neural Message Passing for Quantum Chemistry](https://gitee.com/Zen07/IlyaSutskever-30u30-AI-Paper-Notes/raw/master/resources/imgs/Neural%20Message%20Passing%20for%20Quantum%20Chemistry.png) | 100% | [论文翻译](https://gitee.com/Zen07/IlyaSutskever-30u30-AI-Paper-Notes/blob/master/0-Core-Innovations/Identity%20Mappings%20in%20Deep%20Residual%20Networks/Identity%20Mappings%20in%20Deep%20Residual%20Networks%E4%B8%AD%E8%8B%B1%E5%AF%B9%E7%85%A7%E7%89%88.pdf), [论文解读](https://gitee.com/Zen07/IlyaSutskever-30u30-AI-Paper-Notes/blob/master/0-Core-Innovations/Identity%20Mappings%20in%20Deep%20Residual%20Networks/%E7%A7%92%E6%87%82%EF%BC%81%E4%BD%95%E5%87%AF%E6%98%8E%E7%9A%84%E6%B7%B1%E5%BA%A6%E6%AE%8B%E5%B7%AE%E7%BD%91%E7%BB%9CPPT%E6%98%AF%E8%BF%99%E6%A0%B7%E7%9A%84_ICML2016%20tutorial.pdf) |
| 2025-03-09 | Multi-Scale Context Aggregation by Dilated Convolutions ![Multi-Scale Context Aggregation by Dilated Convolutions](https://gitee.com/Zen07/IlyaSutskever-30u30-AI-Paper-Notes/raw/master/resources/imgs/Multi-Scale%20Context%20Aggregation%20by%20Dilated%20Convolutions.png) | 100% | [论文翻译](https://gitee.com/Zen07/IlyaSutskever-30u30-AI-Paper-Notes/blob/master/1-Applied-NNs/Multi-Scale%20Context%20Aggregation%20by%20Dilated%20Convolutions/Multi-Scale%20Context%20Aggregation%20by%20Dilated%20Convolutions%E4%B8%AD%E8%8B%B1%E5%AF%B9%E7%85%A7%E7%89%88.pdf), [论文解读1](https://gitee.com/Zen07/IlyaSutskever-30u30-AI-Paper-Notes/blob/master/1-Applied-NNs/Multi-Scale%20Context%20Aggregation%20by%20Dilated%20Convolutions/%E3%80%90%E8%AF%AD%E4%B9%89%E5%88%86%E5%89%B2%E4%B8%93%E9%A2%98%E3%80%91%E8%AF%AD%E4%B9%89%E5%88%86%E5%89%B2%E7%9B%B8%E5%85%B3%E5%B7%A5%E4%BD%9C--Multi-Scale%20Context%20Aggregation%20by%20Dilated%20Convolution.pdf), [论文解读2](https://gitee.com/Zen07/IlyaSutskever-30u30-AI-Paper-Notes/blob/master/1-Applied-NNs/Multi-Scale%20Context%20Aggregation%20by%20Dilated%20Convolutions/%E5%AF%B9Dilated%20Convolution%E7%90%86%E8%A7%A3.pdf) |
| 2025-03-07 | Identity Mappings in Deep Residual Networks ![Identity Mappings in Deep Residual Networks](https://gitee.com/Zen07/IlyaSutskever-30u30-AI-Paper-Notes/raw/master/resources/imgs/Identity%20Mappings%20in%20Deep%20Residual%20Networks.png) | 100% | [论文翻译](https://gitee.com/Zen07/IlyaSutskever-30u30-AI-Paper-Notes/blob/master/0-Core-Innovations/Identity%20Mappings%20in%20Deep%20Residual%20Networks/Identity%20Mappings%20in%20Deep%20Residual%20Networks%E4%B8%AD%E8%8B%B1%E5%AF%B9%E7%85%A7%E7%89%88.pdf), [论文解读](https://gitee.com/Zen07/IlyaSutskever-30u30-AI-Paper-Notes/blob/master/0-Core-Innovations/Identity%20Mappings%20in%20Deep%20Residual%20Networks/%E7%A7%92%E6%87%82%EF%BC%81%E4%BD%95%E5%87%AF%E6%98%8E%E7%9A%84%E6%B7%B1%E5%BA%A6%E6%AE%8B%E5%B7%AE%E7%BD%91%E7%BB%9CPPT%E6%98%AF%E8%BF%99%E6%A0%B7%E7%9A%84_ICML2016%20tutorial.pdf) |
| 2025-03-06 | Deep Residual Learning for Image Recognition ![Deep Residual Learning for Image Recognition](https://gitee.com/Zen07/IlyaSutskever-30u30-AI-Paper-Notes/raw/master/resources/imgs/Deep%20Residual%20Learning%20for%20Image%20Recognition.png) | 100% | [论文翻译](https://gitee.com/Zen07/IlyaSutskever-30u30-AI-Paper-Notes/blob/master/0-Core-Innovations/Deep%20Residual%20Learning%20for%20Image%20Recognition/Deep%20Residual%20Learning%20for%20Image%20Recognition%E4%B8%AD%E8%8B%B1%E5%AF%B9%E7%85%A7%E5%8F%8C%E8%AF%AD%E7%89%88.pdf), [论文解读1](https://gitee.com/Zen07/IlyaSutskever-30u30-AI-Paper-Notes/blob/master/0-Core-Innovations/Deep%20Residual%20Learning%20for%20Image%20Recognition/ResNet%EF%BC%88%E6%AE%8B%E5%B7%AE%E7%BD%91%E7%BB%9C%EF%BC%89%E5%87%A0%E4%B8%AA%E5%85%B3%E9%94%AE%E9%97%AE%E9%A2%98%E7%9A%84%E7%90%86%E8%A7%A3_%E6%AE%8B%E5%B7%AE%E7%BD%91%E7%BB%9C%20%E6%81%92%E7%AD%89%E6%98%A0%E5%B0%84.pdf), [论文解读2](https://gitee.com/Zen07/IlyaSutskever-30u30-AI-Paper-Notes/blob/master/0-Core-Innovations/Deep%20Residual%20Learning%20for%20Image%20Recognition/%E8%AE%BA%E6%96%87%E7%B2%BE%E8%AF%BB%E2%80%94%E2%80%94ResNet%20%EF%BC%88Deep%20Residual%20Learning%20for%20Image%20Recognition%EF%BC%89%E6%AE%8B%E5%B7%AE%E7%BD%91%E7%BB%9C_resnet%E8%AE%BA%E6%96%87.pdf)  |
| 2025-03-05 | ImageNet Classification with Deep CNNs ![ImageNet Classifcation with Deep Convolutional Neural Networks](https://gitee.com/Zen07/IlyaSutskever-30u30-AI-Paper-Notes/raw/master/resources/imgs/ImageNet%20Classifcation%20with%20Deep%20Convolutional%20Neural%20Networks.png) | 100% | [论文翻译](https://gitee.com/Zen07/IlyaSutskever-30u30-AI-Paper-Notes/blob/master/1-Applied-NNs/ImageNet%20Classification%20with%20Deep%20Convolutional%20Neural%20Networks/ImageNet%20classification%20with%20deep%20convolutional%20neural%20networks%E4%B8%AD%E8%8B%B1%E5%8F%8C%E8%AF%AD%E5%AF%B9%E7%85%A7%E7%89%88.pdf), [论文解读](https://gitee.com/Zen07/IlyaSutskever-30u30-AI-Paper-Notes/blob/master/1-Applied-NNs/ImageNet%20Classification%20with%20Deep%20Convolutional%20Neural%20Networks/ImageNet%20Classification%20with%20Deep%EF%BC%88PPT%E6%95%99%E6%A1%88%EF%BC%89.pdf), [论文解读视频](https://www.bilibili.com/video/BV1hq4y157t1/?share_source=copy_web&vd_source=ce8447c351cf8b99b86437a6a7708262) |
| 2025-03-05 | Neural Turing Machines ![Neural Turing Machines](https://gitee.com/Zen07/IlyaSutskever-30u30-AI-Paper-Notes/raw/master/resources/imgs/Neural%20Turing%20Machines.png) | 100% | [论文翻译](https://gitee.com/Zen07/IlyaSutskever-30u30-AI-Paper-Notes/blob/master/0-Core-Innovations/Neural%20Turing%20Machines/Neural%20Machine%20Translation%20by%20Jointly%20Learning%20to%20Align%20and%20Translate%E4%B8%AD%E8%8B%B1%E5%8F%8C%E8%AF%AD%E5%AF%B9%E7%85%A7%E7%89%88.pdf), [论文解读1](https://gitee.com/Zen07/IlyaSutskever-30u30-AI-Paper-Notes/blob/master/0-Core-Innovations/Neural%20Turing%20Machines/Neural%20Turing%20Machine%20-%20%E7%A5%9E%E7%BB%8F%E5%9B%BE%E7%81%B5%E6%9C%BA.pdf), [论文解读2](https://gitee.com/Zen07/IlyaSutskever-30u30-AI-Paper-Notes/blob/master/0-Core-Innovations/Neural%20Turing%20Machines/%E7%A5%9E%E7%BB%8F%E7%BD%91%E7%BB%9C%E5%9B%BE%E7%81%B5%E6%9C%BA%E7%9A%84%E9%80%9A%E4%BF%97%E8%A7%A3%E9%87%8A%E5%92%8C%E8%AF%A6%E7%BB%86%E8%BF%87%E7%A8%8B%E5%8F%8A%E5%BA%94%E7%94%A8%EF%BC%9F.pdf) |
| 2025-03-04 | Neural Machine Translation by Jointly Learning to Align and Translate ![Neural Machine Translation by Jointly Learning to Align and Translate](https://gitee.com/Zen07/IlyaSutskever-30u30-AI-Paper-Notes/raw/master/resources/imgs/Neural%20Machine%20Translation%20by%20Jointly%20Learning%20to%20Align%20and%20Translate.png) | 100% | [论文翻译](https://gitee.com/Zen07/IlyaSutskever-30u30-AI-Paper-Notes/blob/master/1-Applied-NNs/Neural%20Machine%20Translation%20by%20Jointly%20Learning%20to%20Align%20and%20Translate/Neural%20Machine%20Translation%20by%20Jointly%20Learning%20to%20Align%20and%20Translate%E4%B8%AD%E8%8B%B1%E5%8F%8C%E8%AF%AD%E5%AF%B9%E7%85%A7%E7%89%88.pdf), [论文解读](https://gitee.com/Zen07/IlyaSutskever-30u30-AI-Paper-Notes/blob/master/1-Applied-NNs/Neural%20Machine%20Translation%20by%20Jointly%20Learning%20to%20Align%20and%20Translate/%E4%B8%80%E6%96%87%E8%AF%BB%E6%87%82%E7%A5%9E%E7%BB%8F%E6%9C%BA%E5%99%A8%E7%BF%BB%E8%AF%91%E7%9A%84%E5%AF%B9%E9%BD%90%E4%B8%8E%E7%BF%BB%E8%AF%91%E8%81%94%E5%90%88%E5%AD%A6%E4%B9%A0.pdf) |
| 2025-03-02 | Pointer Networks ![Pointer Networks](https://gitee.com/Zen07/IlyaSutskever-30u30-AI-Paper-Notes/raw/master/resources/imgs/Pointer%20Networks.png) | 100% | [论文解读1](https://gitee.com/Zen07/IlyaSutskever-30u30-AI-Paper-Notes/blob/master/0-Core-Innovations/Pointer%20Networks/Pointer%20Networks%E7%AE%80%E4%BB%8B%E5%8F%8A%E5%85%B6%E5%BA%94%E7%94%A8.pdf), [论文解读2](https://gitee.com/Zen07/IlyaSutskever-30u30-AI-Paper-Notes/blob/master/0-Core-Innovations/Pointer%20Networks/%E7%BB%8F%E5%85%B8%E8%AE%BA%E6%96%87%E5%9B%9E%E9%A1%BE%E2%80%94%E2%80%94Pointer%20Networks.pdf) |
| 2025-03-01 | Recurrent Neural Network Regularization ![Recurrent Neural Network Regularization](https://gitee.com/Zen07/IlyaSutskever-30u30-AI-Paper-Notes/raw/master/resources/imgs/Recurrent%20Neural%20Network%20Regularization.png) | 100% | [论文解读](https://gitee.com/Zen07/IlyaSutskever-30u30-AI-Paper-Notes/blob/master/0-Core-Innovations/Recurrent%20Neural%20Network%20Regularization/%E8%AE%BA%E6%96%87%E8%A7%A3%E8%AF%BB%EF%BC%9A%E3%80%8A%E9%80%92%E5%BD%92%E7%A5%9E%E7%BB%8F%E7%BD%91%E7%BB%9C%E6%AD%A3%E5%88%99%E5%8C%96%E3%80%8B.pdf) |
| 2025-03-01 | Relational recurrent neural networks ![Relational recurrent neural networks](https://gitee.com/Zen07/IlyaSutskever-30u30-AI-Paper-Notes/raw/master/resources/imgs/Relational%20recurrent%20neural%20networks.png) | 100% | [论文翻译](https://gitee.com/Zen07/IlyaSutskever-30u30-AI-Paper-Notes/blob/master/1-Applied-NNs/Relational%20recurrent%20neural%20networks/DeepMind%E6%8F%90%E5%87%BA%E5%85%B3%E7%B3%BBRNN%EF%BC%9A%E6%9E%84%E5%BB%BA%E5%85%B3%E7%B3%BB%E6%8E%A8%E7%90%86%E6%A8%A1%E5%9D%97%EF%BC%8C%E5%BC%BA%E5%8C%96%E5%AD%A6%E4%B9%A0%E5%88%A9%E5%99%A8.pdf) |
| 2025-02-28 | The Unreasonable Effectiveness of RNNs ![The Unreasonable Effectiveness of RNNs](https://gitee.com/Zen07/IlyaSutskever-30u30-AI-Paper-Notes/raw/master/resources/imgs/The%20Unreasonable%20Effectiveness%20of%20RNNs.png) | 100% | [论文翻译](https://gitee.com/Zen07/IlyaSutskever-30u30-AI-Paper-Notes/blob/master/5-Tutorials/The%20Unreasonable%20Effectiveness%20of%20Recurrent%20Neural%20Networks/RNN%E7%9A%84%E7%A5%9E%E5%A5%87%E4%B9%8B%E5%A4%84%EF%BC%88The%20Unreasonable%20Effectiveness%20of%20Recurrent%20Neural%20Networks%EF%BC%89.pdf) |
| 2025-02-27 | Understanding LSTM Networks ![Understanding LSTM Networks](https://gitee.com/Zen07/IlyaSutskever-30u30-AI-Paper-Notes/raw/master/resources/imgs/Understanding%20LSTM%20Networks.png) | 100% | [论文翻译1](https://gitee.com/Zen07/IlyaSutskever-30u30-AI-Paper-Notes/blob/master/5-Tutorials/Understanding%20LSTM%20Networks/%E4%BA%86%E8%A7%A3LSTM%E7%BD%91%E7%BB%9C%EF%BC%88%E8%8B%B1%E6%96%87%E5%8D%9A%E5%AE%A2%E6%B1%89%E5%8C%96%EF%BC%89.pdf), [论文翻译2](https://gitee.com/Zen07/IlyaSutskever-30u30-AI-Paper-Notes/blob/master/5-Tutorials/Understanding%20LSTM%20Networks/DL%E4%B9%8BLSTM%EF%BC%9A%E3%80%8AUnderstanding%20LSTM%20Networks%E4%BA%86%E8%A7%A3LSTM%E7%BD%91%E7%BB%9C%E3%80%8B%E7%9A%84%E7%BF%BB%E8%AF%91%E5%B9%B6%E8%A7%A3%E8%AF%BB-CSDN%E5%8D%9A%E5%AE%A2.pdf) |

### 下一阶段目标 (2025年Q2)
- 完成Transformer架构相关论文解读
- 添加5篇论文的代码实现示例
- 开发论文关系图谱可视化工具

## 🛠️ 学习工具与资源

### 推荐工具
- **论文阅读**: [Mendeley](https://www.mendeley.com/)、[Connected Papers](https://www.connectedpapers.com/)
- **代码实践**: [Google Colab](https://colab.research.google.com/)、[Kaggle](https://www.kaggle.com/)
- **知识整理**: [Notion](https://www.notion.so/)、[Obsidian](https://obsidian.md/)

### 补充学习资源
- [Papers with Code](https://paperswithcode.com/) - 查找论文相关代码实现
- [Distill.pub](https://distill.pub/) - 深度学习可视化解释
- [李宏毅机器学习课程](https://speech.ee.ntu.edu.tw/~hylee/ml/2021-spring.html) - 中文深度学习详解
- [AI论文精读](https://github.com/mli/paper-reading) - 李沐的论文精读视频

## 🤝 社区协作与资源共享

### 如何有效参与
- **论文解读贡献**: 选择未完成的论文，按照[笔记模板](./resources/templates/paper-notes-template.md)提交高质量解读
- **代码实现分享**: 欢迎提交论文相关的简化实现或应用示例
- **学习资源整理**: 收集视频讲解、博客等补充材料

### 笔记规范
每篇论文的笔记应包含：
- 论文基本信息（标题、作者、年份、链接）
- 核心思想概括（200字以内）
- 创新点分析（3-5点）
- 技术细节（架构图、关键算法、核心公式）
- 实验结果与分析
- 个人理解与启发
- 代码实现笔记（如适用）

### 交流渠道
- [x] [GitHub Discussions](https://github.com/yourusername/IlyaSutskever-30u30-AI-Paper-Notes/discussions)
- 微信群：如果群二维码过期请联系维护者获取邀请，如下

<img src="https://gitee.com/Zen07/IlyaSutskever-30u30-AI-Paper-Notes/raw/master/resources/QRCode/wxq.png" alt="群二维码" width="200">

<img src="https://gitee.com/Zen07/IlyaSutskever-30u30-AI-Paper-Notes/raw/master/resources/QRCode/grwx.png" alt="作者微信" width="200">

## 目录结构
```
├── README.md               # 仓库说明文档
├── recommended-resources.md# 论文的高质量解读资源
├── LICENSE                 # MIT许可证（推荐）
├── .gitignore              # 忽略文件配置
│
├── 0-Core-Innovations/     # 核心神经网络创新
│   ├── Attention-Is-All-You-Need/
│   │   ├── notes.md        # 论文核心思想笔记
│   │   └── code-examples/  # 实现示例代码
│   └── Deep-Residual-Learning/
│
├── 1-Applied-NNs/          # 专业神经网络应用
│   ├── Neural-Machine-Translation/
│   └── Deep-Speech-2/
│
├── 2-Theoretical-Insights/ # 理论见解
│   ├── Scaling-Laws/
│   └── MDL-Principle/
│
├── 3-Interdisciplinary/    # 跨学科研究
│   └── Coffee-Automaton/
│
├── 4-Efficiency&Scalability-Technologies/    # 效率和可扩展性技术
│   └── GPipe: Efficient Training of Giant Neural Networks using Pipeline Parallelism/   
|
├── 5-Tutorials/            # 教材教程
│   ├── CS231n-Notes/
│   └── Annotated-Transformer/
│
└── resources/              # 公共资源
    ├── papers/             # 论文PDF存档
    └── cheatsheets/        # 速查表
```


## 论文难度分级

为帮助读者选择合适的学习起点，我们对论文进行了难度分级：

- ⭐ 入门级：基础概念，适合初学者
- ⭐⭐ 中级：需要一定神经网络基础
- ⭐⭐⭐ 高级：需要扎实的数学和深度学习知识
- ⭐⭐⭐⭐ 专家级：涉及复杂理论和前沿概念


## 贡献指南

欢迎通过以下方式参与项目：

1. Fork本仓库
2. 创建特性分支 (`git checkout -b feature/AmazingFeature`)
3. 提交更改 (`git commit -m '添加了xxx论文的详细笔记'`)
4. 推送到分支 (`git push origin feature/AmazingFeature`)
5. 开启Pull Request

## 许可证

本项目采用MIT许可证 - 详见 [LICENSE](LICENSE) 文件

## 参考文章

在整理和撰写本项目文档时，参考了以下文章：

- **Ilya 的论文阅读清单**  
  链接：[https://zade23.github.io/2024/05/27/【阅读】Ilya的论文阅读清单/](https://zade23.github.io/2024/05/27/【阅读】Ilya的论文阅读清单/)  
  版权声明：本文章采用 [CC BY 4.0 CN](https://creativecommons.org/licenses/by/4.0/deed.zh) 协议，转载请注明出处。
  本项目文档中引用的部分内容遵循 [CC BY 4.0 CN](https://creativecommons.org/licenses/by/4.0/deed.zh) 协议，特此声明。


## 致谢

感谢所有贡献者的辛勤付出，以及Ilya Sutskever提供的这份宝贵论文清单。