{"id":15129926,"url":"https://github.com/mleveryday/practicalai-cn","last_synced_at":"2025-05-14T15:08:41.985Z","repository":{"id":38686757,"uuid":"161311113","full_name":"MLEveryday/practicalAI-cn","owner":"MLEveryday","description":"AI实战-practicalAI 中文版","archived":false,"fork":false,"pushed_at":"2023-12-31T19:26:09.000Z","size":11271,"stargazers_count":4382,"open_issues_count":5,"forks_count":973,"subscribers_count":163,"default_branch":"master","last_synced_at":"2025-04-06T05:05:08.989Z","etag":null,"topics":["deep-learning","google-colab-notebook","jupyter-notebook","machine-learning","pytorch"],"latest_commit_sha":null,"homepage":"","language":"Jupyter 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Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# AI实战-[practicalAI](https://github.com/LisonEvf/practicalAI-cn) 中文版\n[![Colab](https://img.shields.io/badge/launch-Google%20Colab-orange.svg)](https://colab.research.google.com/)\n[![MIT](https://img.shields.io/badge/license-MIT-brightgreen.svg)](https://github.com/LisonEvf/practicalAI-cn/blob/master/LICENSE)\n[![Author](https://img.shields.io/badge/Author-GokuMohandas-blue.svg)](https://github.com/GokuMohandas)\n[![Fork](https://img.shields.io/badge/Fork-MLEveryday/practicalAI--cn-yellow.svg)](https://github.com/MLEveryday/practicalAI-cn)\n\n让你有能力使用机器学习从数据中获取有价值的见解。\n- 🔥 使用 [PyTorch](https://pytorch.org/) 实现基本的机器学习算法和深度神经网络。\n- 🖥️ 不需要任何设置，在浏览器中使用 [Google Colab](https://colab.research.google.com/) 运行所有程序。\n- 📦 不仅仅是教程，而是学习产品级的面向对象机器学习编程。\n\n## Notebooks\n|基础|深度学习|进阶|主题|\n|-|-|-|-|\n|📓 [Notebooks](https://nbviewer.jupyter.org/github/LisonEvf/practicalAI-cn/blob/master/notebooks/00_Notebooks.ipynb)|🔥 [PyTorch](https://nbviewer.jupyter.org/github/LisonEvf/practicalAI-cn/blob/master/notebooks/07_PyTorch.ipynb)|📚 [高级循环神经网络 Advanced RNNs](https://nbviewer.jupyter.org/github/LisonEvf/practicalAI-cn/blob/master/notebooks/14_Advanced_RNNs.ipynb)|📸 [计算机视觉 Computer Vision](https://nbviewer.jupyter.org/github/LisonEvf/practicalAI-cn/blob/master/notebooks/15_Computer_Vision.ipynb)|\n|🐍 [Python](https://nbviewer.jupyter.org/github/LisonEvf/practicalAI-cn/blob/master/notebooks/01_Python.ipynb)|🎛️ [多层感知 Multilayer Perceptrons](https://nbviewer.jupyter.org/github/LisonEvf/practicalAI-cn/blob/master/notebooks/08_Multilayer_Perceptron.ipynb)|🏎️ Highway and Residual Networks|⏰ 时间序列分析 Time Series Analysis|\n|🔢 [NumPy](https://nbviewer.jupyter.org/github/LisonEvf/practicalAI-cn/blob/master/notebooks/02_NumPy.ipynb)|🔎 [数据和模型 Data \u0026 Models](https://nbviewer.jupyter.org/github/LisonEvf/practicalAI-cn/blob/master/notebooks/09_Data_and_Models.ipynb)|🔮 自编码器 Autoencoders|🏘️ Topic Modeling|\n| 🐼 [Pandas](https://nbviewer.jupyter.org/github/LisonEvf/practicalAI-cn/blob/master/notebooks/03_Pandas.ipynb) |📦 [面向对象的机器学习 Object-Oriented ML](https://nbviewer.jupyter.org/github/LisonEvf/practicalAI-cn/blob/master/notebooks/10_Object_Oriented_ML.ipynb)|🎭 生成对抗网络 Generative Adversarial Networks|🛒 推荐系统 Recommendation Systems|\n|📈 [线性回归 Linear Regression](https://nbviewer.jupyter.org/github/LisonEvf/practicalAI-cn/blob/master/notebooks/04_Linear_Regression.ipynb)|🖼️ [卷积神经网络 Convolutional Neural Networks](https://nbviewer.jupyter.org/github/LisonEvf/practicalAI-cn/blob/master/notebooks/11_Convolutional_Neural_Networks.ipynb)|🐝 空间变换模型 Spatial Transformer Networks|🗣️ 预训练语言模型 Pretrained Language Modeling|\n|📊 [逻辑回归 Logistic Regression](https://nbviewer.jupyter.org/github/LisonEvf/practicalAI-cn/blob/master/notebooks/05_Logistic_Regression.ipynb)|📝 [嵌入层 Embeddings](https://nbviewer.jupyter.org/github/LisonEvf/practicalAI-cn/blob/master/notebooks/12_Embeddings.ipynb)||🤷 多任务学习 Multitask Learning|\n|🌳 [随机森林 Random Forests](https://nbviewer.jupyter.org/github/LisonEvf/practicalAI-cn/blob/master/notebooks/06_Random_Forests.ipynb)|📗 [递归神经网络 Recurrent Neural Networks](https://nbviewer.jupyter.org/github/LisonEvf/practicalAI-cn/blob/master/notebooks/13_Recurrent_Neural_Networks.ipynb)||🎯 Low Shot Learning|\n|💥 k-均值聚类 KMeans Clustering|||🍒 强化学习 Reinforcement Learning|\n\n## 查看 notebooks\n\n如果不需要运行 notebooks，使用 Jupyter nbviewer 就可以方便地查看它们。\n\n将 `https://github.com/` 替换为 `https://nbviewer.jupyter.org/github/` ，或者打开 `https://nbviewer.jupyter.org` 并输入 notebook 的 URL。\n\n## 运行 notebooks\n1. 在本项目的 [`notebooks`](/notebooks/) 文件夹获取 notebook；\n2. 你可以在 Google Colab（推荐）或本地电脑运行这些 notebook；\n3. 点击一个 notebook，然后替换URL地址中 `https://github.com/` 为 `https://colab.research.google.com/github/` ，或者使用这个 [Chrome扩展](https://chrome.google.com/webstore/detail/open-in-colab/iogfkhleblhcpcekbiedikdehleodpjo) 一键完成；\n4. 登录你自己的 Google 账户；\n5. 点击工具栏上的 `复制到云端硬盘`，会在一个新的标签页打开 notebook；\n\n\u003cimg src=\"images/copy_to_drive.png\"\u003e\n\n5. 通过去掉标题中的`副本`完成 notebook 重命名；\n6. 运行代码、修改等，所有这些都会自动保存到你的个人 Google Drive。\n\n## 贡献 notebooks\n1. 修改后下载 Google Colab notebook 为 .ipynb 文件；\n\n\u003cimg src=\"images/download_ipynb.png\"\u003e\n\n2. 转到 https://github.com/LisonEvf/practicalAI-cn/tree/master/notebooks ；\n3. 点击 `Upload files`.\n\n\u003cimg src=\"images/upload.png\"\u003e\n\n5. 上传这个 .ipynb 文件；\n6. 写一个详细详细的提交标题和说明；\n7. 适当命名你的分支；\n8. 点击 `Propose changes`。\n\n\u003cimg src=\"images/commit.png\"\u003e\n\n## 贡献列表\n欢迎任何人参与和完善。\n\n|Notebook|译者|\n|--|--|\n|00_Notebooks.ipynb|[@amusi](https://github.com/amusi)|\n|01_Python.ipynb|[@amusi](https://github.com/amusi)|\n|02_NumPy.ipynb|[@amusi](https://github.com/amusi)|\n|03_Pandas.ipynb|[@amusi](https://github.com/amusi)|\n|04_Linear_Regression.ipynb|[@jasonhhao](https://github.com/jasonhhao)|\n|05_Logistic_Regression.ipynb|[@jasonhhao](https://github.com/jasonhhao)|\n|06_Random_Forests.ipynb|[@jasonhhao](https://github.com/jasonhhao)|\n|07_PyTorch.ipynb|[@amusi](https://github.com/amusi)|\n|08_Multilayer_Perceptron.ipynb|[@zhyongquan](https://github.com/zhyongquan)|\n|09_Data_and_Models.ipynb|[@zhyongquan](https://github.com/zhyongquan)|\n|10_Object_Oriented_ML.ipynb|[@zhyongquan](https://github.com/zhyongquan)|\n|11_Convolutional_Neural_Networks.ipynb||\n|12_Embeddings.ipynb|[@wengJJ](https://github.com/wengJJ)|\n|13_Recurrent_Neural_Networks.ipynb||\n|14_Advanced_RNNs.ipynb||\n|15_Computer_Vision.ipynb|||\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmleveryday%2Fpracticalai-cn","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fmleveryday%2Fpracticalai-cn","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmleveryday%2Fpracticalai-cn/lists"}