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practicalai-cn's Introduction

AI实战-practicalAI 中文版

Colab MIT Author Fork

让你有能力使用机器学习从数据中获取有价值的见解。

  • 🔥 使用 PyTorch 实现基本的机器学习算法和深度神经网络。
  • 🖥️ 不需要任何设置,在浏览器中使用 Google Colab 运行所有程序。
  • 📦 不仅仅是教程,而是学习产品级的面向对象机器学习编程。

Notebooks

基础 深度学习 进阶 主题
📓 Notebooks 🔥 PyTorch 📚 高级循环神经网络 Advanced RNNs 📸 计算机视觉 Computer Vision
🐍 Python 🎛️ 多层感知 Multilayer Perceptrons 🏎️ Highway and Residual Networks ⏰ 时间序列分析 Time Series Analysis
🔢 NumPy 🔎 数据和模型 Data & Models 🔮 自编码器 Autoencoders 🏘️ Topic Modeling
🐼 Pandas 📦 面向对象的机器学习 Object-Oriented ML 🎭 生成对抗网络 Generative Adversarial Networks 🛒 推荐系统 Recommendation Systems
📈 线性回归 Linear Regression 🖼️ 卷积神经网络 Convolutional Neural Networks 🐝 空间变换模型 Spatial Transformer Networks 🗣️ 预训练语言模型 Pretrained Language Modeling
📊 逻辑回归 Logistic Regression 📝 嵌入层 Embeddings 🤷 多任务学习 Multitask Learning
🌳 随机森林 Random Forests 📗 递归神经网络 Recurrent Neural Networks 🎯 Low Shot Learning
💥 k-均值聚类 KMeans Clustering 🍒 强化学习 Reinforcement Learning

查看 notebooks

如果不需要运行 notebooks,使用 Jupyter nbviewer 就可以方便地查看它们。

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。

运行 notebooks

  1. 在本项目的 notebooks 文件夹获取 notebook;
  2. 你可以在 Google Colab(推荐)或本地电脑运行这些 notebook;
  3. 点击一个 notebook,然后替换URL地址中 https://github.com/https://colab.research.google.com/github/ ,或者使用这个 Chrome扩展 一键完成;
  4. 登录你自己的 Google 账户;
  5. 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;

  1. 通过去掉标题中的副本完成 notebook 重命名;
  2. 运行代码、修改等,所有这些都会自动保存到你的个人 Google Drive。

贡献 notebooks

  1. 修改后下载 Google Colab notebook 为 .ipynb 文件;

  1. 转到 https://github.com/LisonEvf/practicalAI-cn/tree/master/notebooks
  2. 点击 Upload files.

  1. 上传这个 .ipynb 文件;
  2. 写一个详细详细的提交标题和说明;
  3. 适当命名你的分支;
  4. 点击 Propose changes

贡献列表

欢迎任何人参与和完善。

Notebook 译者
00_Notebooks.ipynb @amusi
01_Python.ipynb @amusi
02_NumPy.ipynb @amusi
03_Pandas.ipynb @amusi
04_Linear_Regression.ipynb @jasonhhao
05_Logistic_Regression.ipynb @jasonhhao
06_Random_Forests.ipynb @jasonhhao
07_PyTorch.ipynb @amusi
08_Multilayer_Perceptron.ipynb @zhyongquan
09_Data_and_Models.ipynb @zhyongquan
10_Object_Oriented_ML.ipynb @zhyongquan
11_Convolutional_Neural_Networks.ipynb
12_Embeddings.ipynb @wengJJ
13_Recurrent_Neural_Networks.ipynb
14_Advanced_RNNs.ipynb
15_Computer_Vision.ipynb

practicalai-cn's People

Contributors

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