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+<!-- 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/karpathy/convnetjs) · [上游 README](https://github.com/karpathy/convnetjs/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。
+
+# ConvNetJS
+
+ConvNetJS 是神经网络的 JavaScript 实现，并附带精美的基于浏览器的演示。目前支持：
+
+- 常见的**神经网络模块**（全连接层、非线性激活）
+- 分类（SVM/Softmax）与回归（L2）**代价函数**（cost functions）
+- 可指定并训练处理图像的**卷积网络**（Convolutional Networks）
+- 基于 Deep Q Learning 的实验性**强化学习**（Reinforcement Learning）模块
+
+更多详细信息请参阅主站 [convnetjs.com](http://convnetjs.com)
+
+**注意**：我已不再积极维护 ConvNetJS，因为我实在没有时间。我认为此时 npm 仓库可能已无法正常工作。
+
+## 在线演示
+- [MNIST 手写数字卷积神经网络](http://cs.stanford.edu/~karpathy/convnetjs/demo/mnist.html)
+- [CIFAR-10 卷积神经网络](http://cs.stanford.edu/~karpathy/convnetjs/demo/cifar10.html)
+- [玩具 2D 数据](http://cs.stanford.edu/~karpathy/convnetjs/demo/classify2d.html)
+- [玩具 1D 回归](http://cs.stanford.edu/~karpathy/convnetjs/demo/regression.html)
+- [在 MNIST 手写数字上训练自编码器](http://cs.stanford.edu/~karpathy/convnetjs/demo/autoencoder.html)
+- [Deep Q Learning 强化学习演示](http://cs.stanford.edu/people/karpathy/convnetjs/demo/rldemo.html)
+- [图像回归（"绘画"）](http://cs.stanford.edu/~karpathy/convnetjs/demo/image_regression.html)
+- [MNIST 上 SGD/Adagrad/Adadelta 对比](http://cs.stanford.edu/people/karpathy/convnetjs/demo/trainers.html)
+
+## 示例代码
+
+下面是一个定义**双层神经网络**并在单个数据点上进行训练的最小示例：
+
+```javascript
+// species a 2-layer neural network with one hidden layer of 20 neurons
+var layer_defs = [];
+// input layer declares size of input. here: 2-D data
+// ConvNetJS works on 3-Dimensional volumes (sx, sy, depth), but if you're not dealing with images
+// then the first two dimensions (sx, sy) will always be kept at size 1
+layer_defs.push({type:'input', out_sx:1, out_sy:1, out_depth:2});
+// declare 20 neurons, followed by ReLU (rectified linear unit non-linearity)
+layer_defs.push({type:'fc', num_neurons:20, activation:'relu'}); 
+// declare the linear classifier on top of the previous hidden layer
+layer_defs.push({type:'softmax', num_classes:10});
+
+var net = new convnetjs.Net();
+net.makeLayers(layer_defs);
+
+// forward a random data point through the network
+var x = new convnetjs.Vol([0.3, -0.5]);
+var prob = net.forward(x); 
+
+// prob is a Vol. Vols have a field .w that stores the raw data, and .dw that stores gradients
+console.log('probability that x is class 0: ' + prob.w[0]); // prints 0.50101
+
+var trainer = new convnetjs.SGDTrainer(net, {learning_rate:0.01, l2_decay:0.001});
+trainer.train(x, 0); // train the network, specifying that x is class zero
+
+var prob2 = net.forward(x);
+console.log('probability that x is class 0: ' + prob2.w[0]);
+// now prints 0.50374, slightly higher than previous 0.50101: the networks
+// weights have been adjusted by the Trainer to give a higher probability to
+// the class we trained the network with (zero)
+```
+
+如果你希望对图像进行预测，这里是一个小型**卷积神经网络**示例：
+
+```javascript
+var layer_defs = [];
+layer_defs.push({type:'input', out_sx:32, out_sy:32, out_depth:3}); // declare size of input
+// output Vol is of size 32x32x3 here
+layer_defs.push({type:'conv', sx:5, filters:16, stride:1, pad:2, activation:'relu'});
+// the layer will perform convolution with 16 kernels, each of size 5x5.
+// the input will be padded with 2 pixels on all sides to make the output Vol of the same size
+// output Vol will thus be 32x32x16 at this point
+layer_defs.push({type:'pool', sx:2, stride:2});
+// output Vol is of size 16x16x16 here
+layer_defs.push({type:'conv', sx:5, filters:20, stride:1, pad:2, activation:'relu'});
+// output Vol is of size 16x16x20 here
+layer_defs.push({type:'pool', sx:2, stride:2});
+// output Vol is of size 8x8x20 here
+layer_defs.push({type:'conv', sx:5, filters:20, stride:1, pad:2, activation:'relu'});
+// output Vol is of size 8x8x20 here
+layer_defs.push({type:'pool', sx:2, stride:2});
+// output Vol is of size 4x4x20 here
+layer_defs.push({type:'softmax', num_classes:10});
+// output Vol is of size 1x1x10 here
+
+net = new convnetjs.Net();
+net.makeLayers(layer_defs);
+
+// helpful utility for converting images into Vols is included
+var x = convnetjs.img_to_vol(document.getElementById('some_image'))
+var output_probabilities_vol = net.forward(x)
+```
+
+## 入门
+
+主站提供了[入门](http://cs.stanford.edu/people/karpathy/convnetjs/started.html) 教程。
+
+完整的[文档](http://cs.stanford.edu/people/karpathy/convnetjs/docs.html) 也可在那里找到。
+
+请参阅本项目的 **releases** 页面以获取压缩编译后的库；下方也提供了直接链接以方便使用（但请自行托管副本）
+
+- [convnet.js](http://cs.stanford.edu/people/karpathy/convnetjs/build/convnet.js)
+- [convnet-min.js](http://cs.stanford.edu/people/karpathy/convnetjs/build/convnet-min.js)
+
+## 从 src/ 编译库到 build/
+
+如果你想为库添加功能，需要修改 `src/` 中的代码，然后将库编译到 `build/` 目录。编译脚本会拼接 `src/` 中的文件，然后压缩结果。
+
+编译通过 ant 任务完成：它会拼接 `src/` 中的源文件来编译 `build/convnet.js`，然后将结果压缩到 `build/convnet-min.js`。请确保已安装 **ant**（在 Ubuntu 上可简单地通过 *sudo apt-get install* 安装），然后 cd 进入 `compile/` 目录并运行：
+
+    $ ant -lib yuicompressor-2.4.8.jar -f build.xml
+
+输出文件将位于 `build/`
+## 在 Node 中使用
+
+该库也可在 *node.js* 中使用：
+
+1. 安装：`$ npm install convnetjs`
+2. 使用：`var convnetjs = require("convnetjs");`
+
+## 许可证
+
+MIT