[PaddlePaddle] Merge master into Paddle branch (d2l-ai#1186)

* change 15.2 title in chinese version (d2l-ai#1109)

change title ’15.2. 情感分析：使用递归神经网络‘ to ’15.2. 情感分析：使用循环神经网络‘

* 修改部分语义表述 (d2l-ai#1105)

* Update r0.17.5 (d2l-ai#1120)

* Bump versions in installation

* 94行typo: （“bert.mall”）->（“bert.small”） (d2l-ai#1129)

* line 313: "bert.mall" -> "bert.small" (d2l-ai#1130)

* fix: update language as native reader (d2l-ai#1114)

* Fix the translation of "stride" (d2l-ai#1115)

* Update index.md (d2l-ai#1118)

修改部分语义表述

* Update self-attention-and-positional-encoding.md (d2l-ai#1133)

依照本书的翻译习惯，将pooling翻译成汇聚

* maybe a comment false (d2l-ai#1149)

* maybe a little false

* maybe a little false

* A minor bug in the rcnn section (Chinese edition) (d2l-ai#1148)

* Update bert.md (d2l-ai#1137)

一个笔误
# 假设batch_size=2，num_pred_positions=3
# 那么batch_idx应该是np.repeat( [0,1], 3 ) = [0,0,0,1,1,1]

* Update calculus.md (d2l-ai#1135)

* fix typo in git documentation (d2l-ai#1106)

* fix: Update the Chinese translation in lr-scheduler.md (d2l-ai#1136)

* Update lr-scheduler.md

* Update chapter_optimization/lr-scheduler.md

Co-authored-by: goldmermaid <goldpiggy@berkeley.edu>

Co-authored-by: goldmermaid <goldpiggy@berkeley.edu>

* fix translation for kaggle-house-price.md (d2l-ai#1107)

* fix translation for kaggle-house-price.md

* fix translation for kaggle-house-price.md

Signed-off-by: sunhaizhou <haizhou.sun@smartmore.com>

* Update weight-decay.md (d2l-ai#1150)

* Update weight-decay.md

关于“k多选d”这一部分，中文读者使用排列组合的方式可能更容易理解
关于“给定k个变量，阶数的个数为...”这句话是有歧义的，不是很像中国话，应该是说“阶数为d的项的个数为...”。
并增加了一句对“因此即使是阶数上的微小变化，比如从$2$到$3$，也会显著增加我们模型的复杂性。”的解释
解释为何会增加复杂性以及为何需要细粒度工具。

* Update chapter_multilayer-perceptrons/weight-decay.md

yep

Co-authored-by: goldmermaid <goldpiggy@berkeley.edu>

* Update chapter_multilayer-perceptrons/weight-decay.md

yep

Co-authored-by: goldmermaid <goldpiggy@berkeley.edu>

Co-authored-by: goldmermaid <goldpiggy@berkeley.edu>

* Fix a spelling error (d2l-ai#1161)

* Update gru.md (d2l-ai#1152)

The key distinction between vanilla RNNs and GRUs is that the latter support gating of the hidden state.
翻译错误

* Unify the function naming (d2l-ai#1113)

Unify naming of the function 'init_xavier()'.

* Update mlp-concise.md (d2l-ai#1166)

* Update mlp-concise.md

语句不通顺

* Update environment.md

语序异常

* Update config.ini

* fix the imprecise description (d2l-ai#1168)

Co-authored-by: yuande <yuande>

* fix typo in chapter_natural-language-processing-pretraining/glove.md (d2l-ai#1175)

* Fix some typos. (d2l-ai#1163)

* Update batch-norm.md (d2l-ai#1170)

fixing typos u->x in article

* Update linear-regression.md (d2l-ai#1090)

We invoke Stuart Russell and Peter Norvig who, in their classic AI text book Artificial Intelligence: A Modern Approach :cite:Russell.Norvig.2016, pointed out that

原译文把who也直接翻译出来了。

* Update mlp.md (d2l-ai#1117)

* Update mlp.md

修改部分语义表述

* Update chapter_multilayer-perceptrons/mlp.md

Co-authored-by: goldmermaid <goldpiggy@berkeley.edu>

* Update chapter_multilayer-perceptrons/mlp.md

Co-authored-by: Aston Zhang <22279212+astonzhang@users.noreply.github.com>
Co-authored-by: goldmermaid <goldpiggy@berkeley.edu>

* Correct a translation error. (d2l-ai#1091)

* Correct a translation error.

* Update chapter_computer-vision/image-augmentation.md

Co-authored-by: Aston Zhang <22279212+astonzhang@users.noreply.github.com>

* Update aws.md (d2l-ai#1121)

* Update aws.md

* Update chapter_appendix-tools-for-deep-learning/aws.md

Co-authored-by: Aston Zhang <22279212+astonzhang@users.noreply.github.com>

* Update image-augmentation.md (d2l-ai#1093)

* Update anchor.md (d2l-ai#1088)

fix a minor issue in  code

* Update anchor.md

* Update image-augmentation.md

* fix typo and improve translation in chapter_linear-networks\softmax-regression.md (d2l-ai#1087)

* Avoid `torch.meshgrid` user warning (d2l-ai#1174)

Avoids the following user warning:
```python
~/anaconda3/envs/torch/lib/python3.10/site-packages/torch/functional.py:568: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at  ../aten/src/ATen/native/TensorShape.cpp:2228.)
  return _VF.meshgrid(tensors, **kwargs)  # type: ignore[attr-defined]
```

* bump to 2.0.0-beta1

* Update sequence.md

* bump beta1 on readme

* Add latex code block background to config

* BLD: Bump python support version 3.9 (d2l-ai#1183)

* BLD: Bump python support version 3.9

* Remove clear and manually downgrade protobuf 4.21.4 to 3.19.4

* BLD: Bump torch and tensorflow

* Update Jenkinsfile

* Update chapter_installation/index.md

* Update chapter_installation/index.md

Co-authored-by: Aston Zhang <22279212+astonzhang@users.noreply.github.com>

* Update config.ini

* Update INFO.md

* Update INFO.md

* Drop mint to show code in pdf, use Inconsolata font, apply code cell color (d2l-ai#1187)

* resolve the conflicts

* revise from publisher (d2l-ai#1089)

* revise from publisher

* d2l api

* post_latex

* revise from publisher

* revise ch11

* Delete d2l-Copy1.bib

* clear cache

* rm d2lbook clear

* debug anchor

* keep original d2l doc

Co-authored-by: Ubuntu <ubuntu@ip-172-31-12-66.us-west-2.compute.internal>
Co-authored-by: Aston Zhang <22279212+astonzhang@users.noreply.github.com>
Co-authored-by: Aston Zhang <asv325@gmail.com>

* 重复语句 (d2l-ai#1188)

Co-authored-by: Aston Zhang <22279212+astonzhang@users.noreply.github.com>

* Improve expression for chapter_preliminaries/pandas.md (d2l-ai#1184)

* Update pandas.md

* Improve expression

* Improve expression

* Update chapter_preliminaries/pandas.md

Co-authored-by: Aston Zhang <22279212+astonzhang@users.noreply.github.com>

* Improce expression for chapter_preliminaries/linear-algebra.md (d2l-ai#1185)

* Improce expression

* Improve code comments

* Update chapter_preliminaries/linear-algebra.md

* Update chapter_preliminaries/linear-algebra.md

* Update chapter_preliminaries/linear-algebra.md

* Update chapter_preliminaries/linear-algebra.md

Co-authored-by: Aston Zhang <22279212+astonzhang@users.noreply.github.com>

* Fix multibox_detection bugs

* Update d2l to 0.17.5 version

* restore older version

* Upgrade pandas

* change to python3.8

* Test warning log

* relocate  warning log

* test logs filtering

* Update gru.md

* Add DeprecationWarning filter

* Test warning log

* Update attention mechanisms & computational performance

* Update multilayer perceptron& linear & convolution networks & computer vision

* Update recurrent&optimition&nlp pretraining & nlp applications

* ignore warnings

* Update index.md

* Update linear networks

* Update multilayer perceptrons&deep learning computation

* Update preliminaries

* Check and Add warning filter

* Update kaggle-cifar10.md

* Update object-detection-dataset.md

* Update ssd.md fcn.md

* Update hybridize.md

* Update hybridize.md

Signed-off-by: sunhaizhou <haizhou.sun@smartmore.com>
Co-authored-by: zhou201505013 <39976863+zhou201505013@users.noreply.github.com>
Co-authored-by: Xinwei Liu <xinzone@outlook.com>
Co-authored-by: Anirudh Dagar <anirudhdagar6@gmail.com>
Co-authored-by: Aston Zhang <22279212+astonzhang@users.noreply.github.com>
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Co-authored-by: gyro永不抽风 <1247006353@qq.com>
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Co-authored-by: Mr. Justice Lawrence John Wargrave <65226618+RUCWargrave@users.noreply.github.com>
Co-authored-by: Chiyuan Fu <fuchiyuan2019@outlook.com>
Co-authored-by: Sunhuashan <48636870+Sunhuashan@users.noreply.github.com>
Co-authored-by: Haiker Sun <haizhou.uestc2011@gmail.com>
Co-authored-by: Ming Liu <akira.liu@njnu.edu.cn>
Co-authored-by: goldmermaid <goldpiggy@berkeley.edu>
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INFO.md

@@ -30,15 +30,13 @@ sudo apt-get install librsvg2-bin
 ```
 
 ```
-wget https://github.com/adobe-fonts/source-han-sans/raw/release/OTF/SourceHanSansSC.zip
-wget https://github.com/adobe-fonts/source-han-serif/raw/release/OTF/SourceHanSerifSC_SB-H.zip
-wget https://github.com/adobe-fonts/source-han-serif/raw/release/OTF/SourceHanSerifSC_EL-M.zip
+wget https://github.com/adobe-fonts/source-han-sans/releases/download/2.004R/SourceHanSansSC.zip
+wget -O SourceHanSerifSC.zip https://github.com/adobe-fonts/source-han-serif/releases/download/2.001R/09_SourceHanSerifSC.zip
 
-unzip SourceHanSansSC.zip
-unzip SourceHanSerifSC_EL-M.zip
-unzip SourceHanSerifSC_SB-H.zip
+unzip SourceHanSansSC.zip -d SourceHanSansSC
+unzip SourceHanSerifSC.zip -d SourceHanSerifSC
 
-sudo mv SourceHanSansSC SourceHanSerifSC_EL-M SourceHanSerifSC_SB-H /usr/share/fonts/opentype/
+sudo mv SourceHanSansSC SourceHanSerifSC /usr/share/fonts/opentype/
 sudo fc-cache -f -v
 ```
 

README.md

@@ -2,7 +2,7 @@
 
 [![Build Status](http://ci.d2l.ai/job/d2l-zh/job/master/badge/icon)](http://ci.d2l.ai/job/d2l-zh/job/master/)
 
-[第一版：zh-v1.D2L.ai](https://zh-v1.d2l.ai/) |  [第二版预览版：zh.D2L.ai](https://zh.d2l.ai)  | 安装和使用书中源代码：[第一版](https://zh-v1.d2l.ai/chapter_prerequisite/install.html) [第二版](https://zh.d2l.ai/chapter_installation/index.html) | 当前版本: v2.0.0-alpha2
+[第一版：zh-v1.D2L.ai](https://zh-v1.d2l.ai/) |  [第二版预览版：zh.D2L.ai](https://zh.d2l.ai)  | 安装和使用书中源代码：[第一版](https://zh-v1.d2l.ai/chapter_prerequisite/install.html) [第二版](https://zh.d2l.ai/chapter_installation/index.html) | 当前版本: v2.0.0-beta1
 
 <h5 align="center"><i>理解深度学习的最佳方法是学以致用。</i></h5>
 

chapter_appendix-tools-for-deep-learning/aws.md

@@ -1,13 +1,13 @@
 # 使用Amazon EC2实例
 :label:`sec_aws`
 
-在本节中，我们将向你展示如何在原始Linux机器上安装所有库。回想一下，在 :numref:`sec_sagemaker`中，我们讨论了如何使用Amazon SageMaker，而在云上自己构建实例的成本更低。本演示包括三个步骤：
+本节将展示如何在原始Linux机器上安装所有库。回想一下， :numref:`sec_sagemaker`讨论了如何使用Amazon SageMaker，而在云上自己构建实例的成本更低。本演示包括三个步骤。
 
 1. 从AWS EC2请求GPU Linux实例。
 1. 安装CUDA（或使用预装CUDA的Amazon机器映像）。
 1. 安装深度学习框架和其他库以运行本书的代码。
 
-此过程也适用于其他实例（和其他云），尽管需要一些细微的修改。在继续操作之前，你需要创建一个aws帐户，有关更多详细信息，请参阅 :numref:`sec_sagemaker`。
+此过程也适用于其他实例（和其他云），尽管需要一些细微的修改。在继续操作之前，你需要创建一个AWS帐户，有关更多详细信息，请参阅 :numref:`sec_sagemaker`。
 
 ## 创建和运行EC2实例
 
@@ -44,7 +44,7 @@
 :width:`700px`
 :label:`fig_ubuntu`
 
-EC2提供了许多不同的实例配置可供选择。对于初学者来说，这有时会让人感到困惑。 :numref:`tab_ec2`列出了不同合适的计算机。
+EC2提供了许多不同的实例配置可供选择。对初学者来说，这有时会让人感到困惑。 :numref:`tab_ec2`列出了不同合适的计算机。
 
 :不同的EC2实例类型
 
@@ -152,7 +152,7 @@ echo "export LD_LIBRARY_PATH=\${LD_LIBRARY_PATH}:/usr/local/cuda/lib64" >> ~/.ba
 
 ## 安装库以运行代码
 
-要运行本书的代码，只需在EC2实例上为linux用户执行 :ref:`chap_installation`中的步骤，并使用以下提示在远程linux服务器上工作：
+要运行本书的代码，只需在EC2实例上为linux用户执行 :ref:`chap_installation`中的步骤，并使用以下提示在远程linux服务器上工作。
 
 * 要在Miniconda安装页面下载bash脚本，请右击下载链接并选择“copy Link address”，然后执行`wget [copied link address]`。
 * 运行`~/miniconda3/bin/conda init`, 你可能需要执行`source~/.bashrc`，而不是关闭并重新打开当前shell。
@@ -185,7 +185,7 @@ jupyter notebook
 
 由于云服务是按使用时间计费的，你应该关闭不使用的实例。请注意，还有其他选择：
 
-* “Stopping”（停止）实例意味着你可以重新启动它。这类似于关闭常规服务器的电源。但是，停止的实例仍将按保留的硬盘空间收取少量费用。
+* “Stopping”（停止）实例意味着你可以重新启动它。这类似于关闭常规服务器的电源。但是，停止的实例仍将按保留的硬盘空间收取少量费用；
 * “Terminating”（终止）实例将删除与其关联的所有数据。这包括磁盘，因此你不能再次启动它。只有在你知道将来不需要它的情况下才这样做。
 
 如果你想要将该实例用作更多实例的模板，请右击 :numref:`fig_connect`中的例子，然后选择“Image”$\rightarrow$“Create”以创建该实例的镜像。完成后，选择“实例状态”$\rightarrow$“终止”以终止实例。下次要使用此实例时，可以按照本节中的步骤基于保存的镜像创建实例。唯一的区别是，在 :numref:`fig_ubuntu`所示的“1.选择AMI”中，你必须使用左侧的“我的AMI”选项来选择你保存的镜像。创建的实例将保留镜像硬盘上存储的信息。例如，你不必重新安装CUDA和其他运行时环境。

chapter_appendix-tools-for-deep-learning/contributing.md

@@ -5,7 +5,7 @@
 如果你发现笔误、无效的链接、一些你认为我们遗漏了引文的地方，
 代码看起来不优雅，或者解释不清楚的地方，请回复我们以帮助读者。
 在常规书籍中，两次印刷之间的间隔（即修订笔误的间隔）常常需要几年，
-但这本书的改进通常需要几个小时到几天的时间。
+但这本书的改进通常需要几小时到几天的时间。
 由于版本控制和持续自动集成（CI）测试，这一切颇为高效。
 为此，你需要向gihub存储库提交一个
 [pull request](https://github.com/d2l-ai/d2l-en/pulls)。

chapter_appendix-tools-for-deep-learning/d2l.md

@@ -30,46 +30,46 @@
 
 ## 模型
 
-```eval_rst 
+```eval_rst
 .. autoclass:: Module
-   :members: 
+   :members:
 
 .. autoclass:: LinearRegressionScratch
    :members:
 
 .. autoclass:: LinearRegression
-   :members:    
+   :members:
 
 .. autoclass:: Classification
    :members:
 ```
 
 ## 数据
 
-```eval_rst 
+```eval_rst
 .. autoclass:: DataModule
-   :members: 
+   :members:
 
 .. autoclass:: SyntheticRegressionData
-   :members: 
+   :members:
 
 .. autoclass:: FashionMNIST
    :members:
 ```
 
 ## 训练
 
-```eval_rst 
+```eval_rst
 .. autoclass:: Trainer
-   :members: 
+   :members:
 
 .. autoclass:: SGD
    :members:
 ```
 
 ## 公用
 
-```eval_rst 
+```eval_rst
 .. autofunction:: add_to_class
 
 .. autofunction:: cpu
@@ -79,7 +79,7 @@
 .. autofunction:: num_gpus
 
 .. autoclass:: ProgressBoard
-   :members: 
+   :members:
 
 .. autoclass:: HyperParameters
    :members:

chapter_appendix-tools-for-deep-learning/index.md

@@ -1,7 +1,7 @@
 # 附录：深度学习工具
 :label:`chap_appendix_tools`
 
-为了充分利用《动手学深度学习》，我们将在本附录中介绍不同工具，
+为了充分利用《动手学深度学习》，本书将在本附录中介绍不同工具，
 例如如何运行这本交互式开源书籍和为本书做贡献。
 
 ```toc

chapter_appendix-tools-for-deep-learning/jupyter.md

@@ -86,7 +86,7 @@ c.NotebookApp.contents_manager_class = 'notedown.NotedownContentsManager'
 ssh myserver -L 8888:localhost:8888
 ```
 
-以上是远程服务器`myserver`的地址。然后我们可以使用http://localhost:8888 访问运行Jupyter Notebook的远程服务器`myserver`。在下一节中，我们将详细介绍如何在AWS实例上运行Jupyter Notebook。 
+以上是远程服务器`myserver`的地址。然后我们可以使用http://localhost:8888 访问运行Jupyter Notebook的远程服务器`myserver`。下一节将详细介绍如何在AWS实例上运行Jupyter Notebook。 
 
 ### 执行时间
 

chapter_appendix-tools-for-deep-learning/sagemaker.md

@@ -1,7 +1,7 @@
 # 使用Amazon SageMaker
 :label:`sec_sagemaker`
 
-深度学习应用程序可能需要很多计算资源，这很容易超出你的本地计算机所能提供的范围。云计算服务允许你使用功能更强大的计算机更轻松地运行本书的GPU密集型代码。本节将介绍如何使用Amazon SageMaker运行本书的代码。
+深度学习程序可能需要很多计算资源，这很容易超出你的本地计算机所能提供的范围。云计算服务允许你使用功能更强大的计算机更轻松地运行本书的GPU密集型代码。本节将介绍如何使用Amazon SageMaker运行本书的代码。
 
 ## 注册