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diff --git a/chapter_installation/index.md b/chapter_installation/index.md
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+# 安装
+:label:`chap_installation`
+
+为了让您开始运行并获得动手学习体验，我们需要为您设置一个运行 Python、Jupyter 笔记本电脑、相关库以及运行本书所需的代码的环境。
+
+## 安装米尼康达
+
+最简单的方法就是安装 [Miniconda](https://conda.io/en/latest/miniconda.html)。Python 3.x 版本是必需的。如果已安装 conda，则可以跳过以下步骤。从网站下载相应的 Miniconda sh 文件，然后使用 `sh <FILENAME> -b` 从命令行执行安装。对于 macOS 用户：
+
+```bash
+# The file name is subject to changes
+sh Miniconda3-latest-MacOSX-x86_64.sh -b
+```
+
+对于 Linux 用户：
+
+```bash
+# The file name is subject to changes
+sh Miniconda3-latest-Linux-x86_64.sh -b
+```
+
+接下来，初始化外壳，以便我们可以直接运行 `conda`。
+
+```bash
+~/miniconda3/bin/conda init
+```
+
+现在关闭并重新打开当前的 shell。您应该能够创建一个新的环境，如下所示：
+
+```bash
+conda create --name d2l -y
+```
+
+## 下载 D2L 笔记本电脑
+
+接下来，我们需要下载这本书的代码。您可以点击任何 HTML 页面顶部的 “所有笔记本” 选项卡下载并解压代码。或者，如果您有 `unzip`（否则运行 `sudo apt install unzip`）可用：
+
+```bash
+mkdir d2l-en && cd d2l-en
+curl https://d2l.ai/d2l-en.zip -o d2l-en.zip
+unzip d2l-en.zip && rm d2l-en.zip
+```
+
+现在我们要激活 `d2l` 环境并安装 `pip`。在此命令后面的查询中输入 `y`。
+
+```bash
+conda activate d2l
+conda install python=3.7 pip -y
+```
+
+## 安装框架和 `d2l` 软件包
+
+:begin_tab:`mxnet,pytorch`
+在安装深度学习框架之前，请先检查您的计算机上是否有正确的 GPU（在标准笔记本电脑上为显示器提供电源的 GPU 不计入我们的目的）。如果要在 GPU 服务器上安装，请继续执行 :ref:`subsec_gpu` 以获取有关安装 GPU 支持版本的说明。
+
+否则，您可以安装 CPU 版本。这将是足够的马力来帮助您完成前几章，但您需要在运行更大的模型之前访问 GPU。
+:end_tab:
+
+:begin_tab:`mxnet`
+```bash
+pip install mxnet==1.6.0
+```
+:end_tab:
+
+:begin_tab:`pytorch`
+```bash
+pip install torch==1.5.1 torchvision -f https://download.pytorch.org/whl/torch_stable.html
+```
+:end_tab:
+
+:begin_tab:`tensorflow`
+您可以通过以下方式安装具有 CPU 和 GPU 支持的腾讯流：
+
+```bash
+pip install tensorflow==2.2.0 tensorflow-probability==0.10.0
+```
+:end_tab:
+
+我们还安装了 `d2l` 软件包，它封装了本书中常用的函数和类。
+
+```bash
+pip install -U d2l
+```
+
+安装完成后，我们现在通过运行以下操作打开 Jupyter 笔记本：
+
+```bash
+jupyter notebook
+```
+
+此时，您可以在 Web 浏览器中打开 http://localhost:8888（通常会自动打开）。然后我们可以运行这本书的每个部分的代码。在运行书籍代码或更新深度学习框架或 `d2l` 软件包之前，请始终执行 `conda activate d2l` 以激活运行时环境。要退出环境，请运行 `conda deactivate`。
+
+## GPU 支持
+:label:`subsec_gpu`
+
+:begin_tab:`mxnet,pytorch`
+默认情况下，安装深度学习框架时不支持 GPU，以确保它在任何计算机（包括大多数笔记本电脑）上运行。本书的一部分要求或建议使用 GPU 运行。如果您的计算机具有 NVIDIA 显卡并且已安装 [CUDA](https://developer.nvidia.com/cuda-downloads)，则应安装启用 GPU 的版本。如果您已经安装了仅 CPU 版本，则可能需要首先通过运行以下操作将其删除：
+:end_tab:
+
+:begin_tab:`tensorflow`
+默认情况下，TensorFlow 安装了 GPU 支持。如果您的计算机具有 NVIDIA 显卡并且已安装 [CUDA](https://developer.nvidia.com/cuda-downloads)，那么您都可以完成。
+:end_tab:
+
+:begin_tab:`mxnet`
+```bash
+pip uninstall mxnet
+```
+:end_tab:
+
+:begin_tab:`pytorch`
+```bash
+pip uninstall torch
+```
+:end_tab:
+
+:begin_tab:`mxnet,pytorch`
+然后，我们需要找到您安装的 CUDA 版本。你可以通过 `nvcc --version` 或 `cat /usr/local/cuda/version.txt` 查看它。假定您已安装 CUDA 10.1，则可以使用以下命令进行安装：
+:end_tab:
+
+:begin_tab:`mxnet`
+```bash
+# For Windows users
+pip install mxnet-cu101==1.6.0b20190926
+
+# For Linux and macOS users
+pip install mxnet-cu101==1.6.0
+```
+:end_tab:
+
+:begin_tab:`pytorch`
+```bash
+pip install torch==1.5.1+cu101 -f https://download.pytorch.org/whl/torch_stable.html
+```
+:end_tab:
+
+:begin_tab:`mxnet,pytorch`
+您可以根据您的 CUDA 版本更改最后一位数字，例如：CUDA 10.0 的 `cu100` 和 CUDA 9.0 的 `cu90`。
+:end_tab:
+
+## 练习
+
+1. 下载该书的代码并安装运行时环境。
+
+:begin_tab:`mxnet`
+[Discussions](https://discuss.d2l.ai/t/23)
+:end_tab:
+
+:begin_tab:`pytorch`
+[Discussions](https://discuss.d2l.ai/t/24)
+:end_tab:
+
+:begin_tab:`tensorflow`
+[Discussions](https://discuss.d2l.ai/t/436)
+:end_tab:
diff --git a/chapter_installation/index_origin.md b/chapter_installation/index_origin.md
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+# Installation
+:label:`chap_installation`
+
+In order to get you up and running for hands-on learning experience,
+we need to set you up with an environment for running Python,
+Jupyter notebooks, the relevant libraries,
+and the code needed to run the book itself.
+
+## Installing Miniconda
+
+The simplest way to get going will be to install
+[Miniconda](https://conda.io/en/latest/miniconda.html). The Python 3.x version
+is required. You can skip the following steps if conda has already been installed.
+Download the corresponding Miniconda sh file from the website
+and then execute the installation from the command line
+using `sh <FILENAME> -b`. For macOS users:
+
+```bash
+# The file name is subject to changes
+sh Miniconda3-latest-MacOSX-x86_64.sh -b
+```
+
+
+For Linux users:
+
+```bash
+# The file name is subject to changes
+sh Miniconda3-latest-Linux-x86_64.sh -b
+```
+
+
+Next, initialize the shell so we can run `conda` directly.
+
+```bash
+~/miniconda3/bin/conda init
+```
+
+
+Now close and re-open your current shell. You should be able to create a new
+environment as following:
+
+```bash
+conda create --name d2l -y
+```
+
+
+## Downloading the D2L Notebooks
+
+Next, we need to download the code of this book. You can click the "All
+Notebooks" tab on the top of any HTML page to download and unzip the code.
+Alternatively, if you have `unzip` (otherwise run `sudo apt install unzip`) available:
+
+```bash
+mkdir d2l-en && cd d2l-en
+curl https://d2l.ai/d2l-en.zip -o d2l-en.zip
+unzip d2l-en.zip && rm d2l-en.zip
+```
+
+
+Now we will want to activate the `d2l` environment and install `pip`.
+Enter `y` for the queries that follow this command.
+
+```bash
+conda activate d2l
+conda install python=3.7 pip -y
+```
+
+
+## Installing the Framework and the `d2l` Package
+
+:begin_tab:`mxnet,pytorch`
+Before installing the deep learning framework, please first check
+whether or not you have proper GPUs on your machine
+(the GPUs that power the display on a standard laptop
+do not count for our purposes).
+If you are installing on a GPU server,
+proceed to :ref:`subsec_gpu` for instructions
+to install a GPU-supported version.
+
+Otherwise, you can install the CPU version.
+That will be more than enough horsepower to get you
+through the first few chapters but you will want
+to access GPUs before running larger models.
+:end_tab:
+
+
+:begin_tab:`mxnet`
+
+```bash
+pip install mxnet==1.6.0
+```
+
+
+:end_tab:
+
+:begin_tab:`pytorch`
+
+```bash
+pip install torch==1.5.1 torchvision -f https://download.pytorch.org/whl/torch_stable.html
+```
+
+
+:end_tab:
+
+:begin_tab:`tensorflow`
+You can install TensorFlow with both CPU and GPU support via the following:
+
+```bash
+pip install tensorflow==2.2.0 tensorflow-probability==0.10.0
+```
+
+
+:end_tab:
+
+
+We also install the `d2l` package that encapsulates frequently used
+functions and classes in this book.
+
+```bash
+pip install -U d2l
+```
+
+
+Once they are installed, we now open the Jupyter notebook by running:
+
+```bash
+jupyter notebook
+```
+
+
+At this point, you can open http://localhost:8888 (it usually opens automatically) in your Web browser. Then we can run the code for each section of the book.
+Please always execute `conda activate d2l` to activate the runtime environment
+before running the code of the book or updating the deep learning framework or the `d2l` package.
+To exit the environment, run `conda deactivate`.
+
+
+## GPU Support
+:label:`subsec_gpu`
+
+:begin_tab:`mxnet,pytorch`
+By default, the deep learning framework is installed without GPU support
+to ensure that it will run on any computer (including most laptops).
+Part of this book requires or recommends running with GPU.
+If your computer has NVIDIA graphics cards and has installed [CUDA](https://developer.nvidia.com/cuda-downloads),
+then you should install a GPU-enabled version.
+If you have installed the CPU-only version,
+you may need to remove it first by running:
+:end_tab:
+
+:begin_tab:`tensorflow`
+By default, TensorFlow is installed with GPU support.
+If your computer has NVIDIA graphics cards and has installed [CUDA](https://developer.nvidia.com/cuda-downloads),
+then you are all set.
+:end_tab:
+
+:begin_tab:`mxnet`
+
+```bash
+pip uninstall mxnet
+```
+
+
+:end_tab:
+
+:begin_tab:`pytorch`
+
+```bash
+pip uninstall torch
+```
+
+
+:end_tab:
+
+:begin_tab:`mxnet,pytorch`
+Then we need to find the CUDA version you installed.
+You may check it through `nvcc --version` or `cat /usr/local/cuda/version.txt`.
+Assume that you have installed CUDA 10.1,
+then you can install with the following command:
+:end_tab:
+
+:begin_tab:`mxnet`
+
+```bash
+# For Windows users
+pip install mxnet-cu101==1.6.0b20190926
+
+# For Linux and macOS users
+pip install mxnet-cu101==1.6.0
+```
+
+
+:end_tab:
+
+:begin_tab:`pytorch`
+
+```bash
+pip install torch==1.5.1+cu101 -f https://download.pytorch.org/whl/torch_stable.html
+```
+
+
+:end_tab:
+
+:begin_tab:`mxnet,pytorch`
+You may change the last digits according to your CUDA version, e.g., `cu100` for
+CUDA 10.0 and `cu90` for CUDA 9.0.
+:end_tab:
+
+## Exercises
+
+1. Download the code for the book and install the runtime environment.
+
+:begin_tab:`mxnet`
+[Discussions](https://discuss.d2l.ai/t/23)
+:end_tab:
+
+:begin_tab:`pytorch`
+[Discussions](https://discuss.d2l.ai/t/24)
+:end_tab:
+
+:begin_tab:`tensorflow`
+[Discussions](https://discuss.d2l.ai/t/436)
+:end_tab: