docs: make Chinese README the default

README.md

@@ -1,49 +1,55 @@
-# Machine learning algorithms
+<!-- WEHUB_ZH_README -->
-A collection of minimal and clean implementations of machine learning algorithms.
+> [!NOTE]
+> 本文档由 WeHub 基于上游 README 翻译整理，属于社区翻译，非官方中文文档。  
+> [English](./README.en.md) · [原始项目](https://github.com/rushter/MLAlgorithms) · [上游 README](https://github.com/rushter/MLAlgorithms/blob/HEAD/README.md)  
+> 原作者、版权与许可证归属以原始项目及本仓库 LICENSE 文件为准。
 
-### Why?
+# 机器学习算法
-This project is targeting people who want to learn internals of ml algorithms or implement them from scratch.  
+一组简洁、清晰的机器学习算法实现。
-The code is much easier to follow than the optimized libraries and easier to play with.  
-All algorithms are implemented in Python, using numpy, scipy and autograd.  
 
-### Implemented:
+### 为什么？
-* [Deep learning (MLP, CNN, RNN, LSTM)](mla/neuralnet)
+本项目面向希望学习机器学习（ML）算法内部原理或从零实现这些算法的人。  
-* [Linear regression, logistic regression](mla/linear_models.py)
+相比优化过的库，这里的代码更易读懂，也更便于动手试验。  
-* [Random Forests](mla/ensemble/random_forest.py)
+所有算法均使用 Python 实现，依赖 numpy、scipy 和 autograd。  
-* [Support vector machine (SVM) with kernels (Linear, Poly, RBF)](mla/svm)
+
+### 已实现：
+* [深度学习（MLP、CNN、RNN、LSTM）](mla/neuralnet)
+* [线性回归、逻辑回归](mla/linear_models.py)
+* [随机森林（Random Forests）](mla/ensemble/random_forest.py)
+* [支持向量机（SVM）及核函数（Linear、Poly、RBF）](mla/svm)
 * [K-Means](mla/kmeans.py)
-* [Gaussian Mixture Model](mla/gaussian_mixture.py)
+* [高斯混合模型（Gaussian Mixture Model）](mla/gaussian_mixture.py)
-* [K-nearest neighbors](mla/knn.py)
+* [K 近邻（K-nearest neighbors）](mla/knn.py)
-* [Naive bayes](mla/naive_bayes.py)
+* [朴素贝叶斯（Naive Bayes）](mla/naive_bayes.py)
-* [Principal component analysis (PCA)](mla/pca.py)
+* [主成分分析（PCA）](mla/pca.py)
-* [Factorization machines](mla/fm.py)
+* [因子分解机（Factorization machines）](mla/fm.py)
-* [Restricted Boltzmann machine (RBM)](mla/rbm.py)
+* [受限玻尔兹曼机（RBM）](mla/rbm.py)
-* [t-Distributed Stochastic Neighbor Embedding (t-SNE)](mla/tsne.py)
+* [t 分布随机邻域嵌入（t-SNE）](mla/tsne.py)
-* [Gradient Boosting trees (also known as GBDT, GBRT, GBM, XGBoost)](mla/ensemble/gbm.py)
+* [梯度提升树（亦称 GBDT、GBRT、GBM、XGBoost）](mla/ensemble/gbm.py)
-* [Reinforcement learning (Deep Q learning)](mla/rl)
+* [强化学习（Deep Q learning）](mla/rl)
 
 
-### Installation
+### 安装
 ```sh
         git clone https://github.com/rushter/MLAlgorithms
         cd MLAlgorithms
         pip install scipy numpy
         python setup.py develop
 ```
-### How to run examples without installation
+### 如何在不安装的情况下运行示例
 ```sh
         cd MLAlgorithms
         python -m examples.linear_models
 ```
-### How to run examples within Docker
+### 如何在 Docker 中运行示例
 ```sh
         cd MLAlgorithms
         docker build -t mlalgorithms .
         docker run --rm -it mlalgorithms bash
         python -m examples.linear_models
 ```
-### Contributing
+### 贡献
 
-Your contributions are always welcome!  
+欢迎贡献！  
-Feel free to improve existing code, documentation or implement new algorithm.  
+欢迎改进现有代码与文档，或实现新算法。  
-Please open an issue to propose your changes if they are big enough.  
+若改动较大，请先开 issue 说明你的方案。