docs: make Chinese README the default

README.md

@@ -1,12 +1,17 @@
+<!-- WEHUB_ZH_README -->
+> [!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/eriklindernoren/ML-From-Scratch) · [上游 README](https://github.com/eriklindernoren/ML-From-Scratch/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。
+
 # Machine Learning From Scratch
 
-## About
-Python implementations of some of the fundamental Machine Learning models and algorithms from scratch.
+## 关于
+一些基础机器学习（Machine Learning）模型与算法的 Python 从零实现。
 
-The purpose of this project is not to produce as optimized and computationally efficient algorithms as possible
-but rather to present the inner workings of them in a transparent and accessible way.
+本项目的目的并非尽可能产出优化且计算高效的算法，而是以透明、易懂的方式呈现它们的内部工作原理。
 
-## Table of Contents
+## 目录
 - [Machine Learning From Scratch](#machine-learning-from-scratch)
   * [About](#about)
   * [Table of Contents](#table-of-contents)
@@ -28,24 +33,23 @@ but rather to present the inner workings of them in a transparent and accessible
     + [Deep Learning](#deep-learning)
   * [Contact](#contact)
 
-## Installation
+## 安装
     $ git clone https://github.com/eriklindernoren/ML-From-Scratch
     $ cd ML-From-Scratch
     $ python setup.py install
 
-## Examples
-### Polynomial Regression
+## 示例
+### 多项式回归（Polynomial Regression）
     $ python mlfromscratch/examples/polynomial_regression.py
 
 <p align="center">
     <img src="http://eriklindernoren.se/images/p_reg.gif" width="640"\>
 </p>
 <p align="center">
-    Figure: Training progress of a regularized polynomial regression model fitting <br>
-    temperature data measured in Linköping, Sweden 2016.
+    图：正则化多项式回归模型拟合瑞典林雪平（Linköping）2016 年测得温度数据的训练过程。<br>
 </p>
 
-### Classification With CNN
+### 基于 CNN 的分类（Classification With CNN）
     $ python mlfromscratch/examples/convolutional_neural_network.py
 
     +---------+
@@ -80,20 +84,20 @@ but rather to present the inner workings of them in a transparent and accessible
     <img src="http://eriklindernoren.se/images/mlfs_cnn1.png" width="640">
 </p>
 <p align="center">
-    Figure: Classification of the digit dataset using CNN.
+    图：使用 CNN 对数字数据集进行分类。<br>
 </p>
 
-### Density-Based Clustering
+### 基于密度的聚类（Density-Based Clustering）
     $ python mlfromscratch/examples/dbscan.py
 
 <p align="center">
     <img src="http://eriklindernoren.se/images/mlfs_dbscan.png" width="640">
 </p>
 <p align="center">
-    Figure: Clustering of the moons dataset using DBSCAN.
+    图：使用 DBSCAN 对 moons 数据集进行聚类。<br>
 </p>
 
-### Generating Handwritten Digits
+### 生成手写数字（Generating Handwritten Digits）
     $ python mlfromscratch/unsupervised_learning/generative_adversarial_network.py
 
     +-----------+
@@ -140,11 +144,10 @@ but rather to present the inner workings of them in a transparent and accessible
     <img src="http://eriklindernoren.se/images/gan_mnist5.gif" width="640">
 </p>
 <p align="center">
-    Figure: Training progress of a Generative Adversarial Network generating <br>
-    handwritten digits.
+    图：生成手写数字的生成对抗网络（Generative Adversarial Network）训练过程。<br>
 </p>
 
-### Deep Reinforcement Learning
+### 深度强化学习（Deep Reinforcement Learning）
     $ python mlfromscratch/examples/deep_q_network.py
 
     +----------------+
@@ -164,21 +167,20 @@ but rather to present the inner workings of them in a transparent and accessible
     <img src="http://eriklindernoren.se/images/mlfs_dql1.gif" width="640">
 </p>
 <p align="center">
-    Figure: Deep Q-Network solution to the CartPole-v1 environment in OpenAI gym.
+    图：Deep Q-Network 在 OpenAI gym 的 CartPole-v1 环境中的求解结果。<br>
 </p>
 
-### Image Reconstruction With RBM
+### 使用 RBM 进行图像重建（Image Reconstruction With RBM）
     $ python mlfromscratch/examples/restricted_boltzmann_machine.py
 
 <p align="center">
     <img src="http://eriklindernoren.se/images/rbm_digits1.gif" width="640">
 </p>
 <p align="center">
-    Figure: Shows how the network gets better during training at reconstructing <br>
-    the digit 2 in the MNIST dataset.
+    图：展示网络在训练过程中如何更好地重建 MNIST 数据集中的数字 2。<br>
 </p>
 
-### Evolutionary Evolved Neural Network
+### 进化演化的神经网络（Evolutionary Evolved Neural Network）
     $ python mlfromscratch/examples/neuroevolution.py
 
     +---------------+
@@ -209,8 +211,7 @@ but rather to present the inner workings of them in a transparent and accessible
     <img src="http://eriklindernoren.se/images/evo_nn4.png" width="640">
 </p>
 <p align="center">
-    Figure: Classification of the digit dataset by a neural network which has<br>
-    been evolutionary evolved.
+    图：由经进化演化的神经网络对数字数据集进行的分类。<br>
 </p>
 
 ### Genetic Algorithm
@@ -219,13 +220,7 @@ but rather to present the inner workings of them in a transparent and accessible
     +--------+
     |   GA   |
     +--------+
-    Description: Implementation of a Genetic Algorithm which aims to produce
-    the user specified target string. This implementation calculates each
-    candidate's fitness based on the alphabetical distance between the candidate
-    and the target. A candidate is selected as a parent with probabilities proportional
-    to the candidate's fitness. Reproduction is implemented as a single-point
-    crossover between pairs of parents. Mutation is done by randomly assigning
-    new characters with uniform probability.
+    描述：遗传算法（Genetic Algorithm）的实现，旨在生成用户指定的目标字符串。该实现根据候选字符串与目标之间的字母距离计算每个候选的适应度。候选个体作为父代被选中的概率与其适应度成正比。繁殖通过父代对之间的单点交叉实现。突变则以均匀概率随机分配新字符。
 
     Parameters
     ----------
@@ -248,9 +243,9 @@ but rather to present the inner workings of them in a transparent and accessible
     +-------------+
     |   Apriori   |
     +-------------+
-    Minimum Support: 0.25
-    Minimum Confidence: 0.8
-    Transactions:
+    最小支持度：0.25
+    最小置信度：0.8
+    事务：
         [1, 2, 3, 4]
         [1, 2, 4]
         [1, 2]
@@ -258,9 +253,9 @@ but rather to present the inner workings of them in a transparent and accessible
         [2, 3]
         [3, 4]
         [2, 4]
-    Frequent Itemsets:
+    频繁项集：
         [1, 2, 3, 4, [1, 2], [1, 4], [2, 3], [2, 4], [3, 4], [1, 2, 4], [2, 3, 4]]
-    Rules:
+    规则：
         1 -> 2 (support: 0.43, confidence: 1.0)
         4 -> 2 (support: 0.57, confidence: 0.8)
         [1, 4] -> 2 (support: 0.29, confidence: 1.0)