[CodeStyle][Typos][C-[1-5]] Fix typo(startswith C) (#7573)

* Fix-c-1-5

* fix_HOT_1

* del_typos

Author: Ricardo-shuo-liu

_typos.toml

@@ -28,9 +28,6 @@ Archetecture = "Archetecture"
 Asynchoronous = "Asynchoronous"
 Attrbute = "Attrbute"
 Attribtue = "Attribtue"
-Classfication = "Classfication"
-Comparision = "Comparision"
-Contructing = "Contructing"
 Creenshot = "Creenshot"
 Embeddding = "Embeddding"
 Embeding = "Embeding"
@@ -70,10 +67,7 @@ Ture = "Ture"
 accordding = "accordding"
 accoustic = "accoustic"
 accpetance = "accpetance"
-caculate = "caculate"
 cantains = "cantains"
-choosen = "choosen"
-classfication = "classfication"
 classfy = "classfy"
 cliping = "cliping"
 colunms = "colunms"

docs/design/concurrent/select_op.md

@@ -6,7 +6,7 @@ In golang, the [**select**](https://golang.org/ref/spec#Select_statements)
 statement lets a goroutine wait on multiple communication operations at the
 same time. The **select** blocks until one of its cases can run, then
 executes the case.  If multiple cases are ready to run, then one case is
-choosen at random to be executed.
+chosen at random to be executed.
 
 With the introduction of CSP for Paddle, we mimic this behavior by
 creating a ***select_op***.

docs/design/modules/evaluator.md

@@ -16,7 +16,7 @@ Currently, every operation is expressed in the graph. We divide the evaluator pr
 
 ## Implementation
 This design is shown in the Python API.
-Each metric operator needs to caculate the metric statistic and return the batch-aware states. Python side is responsible for accumulating the states for each pass.
+Each metric operator needs to calculate the metric statistic and return the batch-aware states. Python side is responsible for accumulating the states for each pass.
 
 
 ```python
@@ -37,7 +37,7 @@ class Evaluator:
 
     def _update_ops(self, input, label, **kwargs)
        """
-       Add mini-batch evaluator caculate operators to the main_program.
+       Add mini-batch evaluator calculate operators to the main_program.
        Add increment operator to accumulate the metric states.
        """
 

docs/design/modules/python_api.md

@@ -225,7 +225,7 @@ A mechanism to reuse code between layer functions is necessary. It will be aroun
 
 
 
-### Comparision between global functions and helper class
+### Comparison between global functions and helper class
 
 The `FullyConnected` layer will be as follow when we provide global functions:
 

docs/eval/evaluation_of_docs_system.md

@@ -28,7 +28,7 @@ TensorFlow 的文档规划，比较直接地匹配了本文所介绍的分类标
 - Quickstart for beginners
 - Quickstart for experts
 - ML basics with Keras
-    - Basic image classfication
+    - Basic image classification
     - Basic text classification
     - Text classification with TF Hub
     - Regression
@@ -81,7 +81,7 @@ TensorFlow 的文档规划，比较直接地匹配了本文所介绍的分类标
     - Generate music with an RNN
 - Structured data
     - Classifying structured data with preprocessing layers
-    - Classfication on imbalanced data
+    - Classification on imbalanced data
     - Time series forecasting
     - Decision forest models
     - Recommenders
@@ -546,15 +546,15 @@ MindSpore 的有自己独立的文档分类标准和风格，所以硬套本文
 |                              | TensorFlow                                                   | 数量 | PyTorch                                                      | 数量 | MindSpore                                                    | 数量 | PaddlePaddle                                                 | 数量   |
 | ---------------------------- | ------------------------------------------------------------ | ---- | ------------------------------------------------------------ | ---- | ------------------------------------------------------------ | ---- | ------------------------------------------------------------ | ------ |
 | 基本数据（Tensor）和基本算子 | Tensors Variables Tensor slicing Ragged tensor Sparse tensor DTensor concepts | 6    | Tensors Transforms Introduction to PyTorch Tensors           | 3    | 张量 Tensor                                                  | 1    | Tensor 概念介绍                                               | 1      |
-| 数据加载与预处理             | Images CSV Numpy pandas.DataFrame TFRecord and tf.Example Additional formats with tf.io Text More text loading Classifying structured data with preprocessing layers Classfication on imbalanced data Time series forecasting Decision forest models | 13   | Datasets & Dataloaders                                       | 1    | 数据处理 数据处理（进阶） 自动数据增强 轻量化数据处理 单节点数据缓存 优化数据处理 | 6    | 数据集的定义和加载 数据预处理                                | 2      |
+| 数据加载与预处理             | Images CSV Numpy pandas.DataFrame TFRecord and tf.Example Additional formats with tf.io Text More text loading Classifying structured data with preprocessing layers Classification on imbalanced data Time series forecasting Decision forest models | 13   | Datasets & Dataloaders                                       | 1    | 数据处理 数据处理（进阶） 自动数据增强 轻量化数据处理 单节点数据缓存 优化数据处理 | 6    | 数据集的定义和加载 数据预处理                                | 2      |
 | 如何组网                     | Modules, layers, and models                                  | 1    | Build the Neural Network Building Models with PyTorch What is torch.nn really? Learing PyTorch with Examples | 4    | 创建网络 网络构建                                            | 2    | 模型组网 飞桨高层 API 使用指南 层与模型                      | 3      |
 | 如何训练                     | Training loops NumPy API Checkpoint SavedModel               | 4    | Optimization Model Parameters Traning with PyTorch           | 2    | 模型训练 训练与评估                                          | 2    | 训练与预测验证 自定义指标                                    | 2      |
 | 保存与加载模型               | Save and load Save and load(Distributed Training)             | 2    | Save and Load the Model                                      | 1    | 保存与加载                                                   | 1    | 模型保存与载入 模型保存及加载（应用实践）                    | 2      |
 | 可视化、调优技巧             | Overfit and underfit Tune hyperprameters with Keras Tuner Better performance with tf.function Profile TensorFlow performance Graph optimizaition Optimize GPU Performance Mixed precision | 7    | PyTorch TensorBoard Support Model Understanding with Captum Visualizing Models, Data, and Training with TensorBoard Profiling your PyTorch Module PyTorch Profiler with TensorBoard Hyperparameter tuning with Ray Tune Optimizing Vision Transformer Model for Deployment Parametrization Tutorial Pruning Tutorial Grokking PyTorch Intel CPU performance from first principles | 11   | 查看中间文件 Dump 功能调试 自定义调试信息 调用自定义类 算子增量编译 算子调优工具 自动数据加速 固定随机性以复现脚本运行结果 | 8    | VisualDL 工具简介 VisualDL 使用指南 飞桨模型量化             | 3      |
 | 自动微分                     | Automatic differentiation Advanced autodiff                  | 2    | Automatic Differentiation with torch.autograd The Fundamentals of Autograd | 2    | 自动微分                                                     | 1    | 自动微分                                                     | 1      |
 | 动态图与静态图               | Graphs and functions                                         | 1    | （torchscript 其实是静态图，不过归类到部署中了）             | 0    | 动态图与静态图                                               | 1    | 使用样例 转换原理 支持语法 案例解析 报错调试 动态图 使用动转静完成以图搜图 | 7      |
 | 部署相关                     | https://www.tensorflow.org/tfx/tutorials 下的 21 篇文章 https://www.tensorflow.org/tfx/guide 下的 30+文章 | 50+  | Deploying PyTorch in Python via a REST API with Flask Introduction to TorchScript Loading a TorchScript Model in C++ (optional) Exporting a Model from PyTorch to ONNX and Running it using ONNX Runtime Real Time Inference on Raspberry Pi 4 | 6    | 推理与部署 模型推理总览 GPU/CPU 推理 Ascend 910 AI 处理器上推理 Ascend 310 AI 处理器上使用 MindIR 模型进行推理 Ascend 310 AI 处理器上使用 AIR 模型进行推理 | 7    | 服务器部署 移动端/嵌入式部署 模型压缩 https://www.paddlepaddle.org.cn/lite/v2.10/guide/introduction.html 下 50+ 篇文章 | 50+    |
-| CV 领域相关                  | Basic image classfication Convolutional Neural Network Image classification Transfer learning and fine-tuning Transfer learning with TF Hub Data Augmentaion Image segmentation Object detection with TF Hub Neural style transfer DeepDream DCGAN Pix2Pix CycleGAN Adversarial FGSM Intro to Autoencoders Variational Autoencoder | 16   | TorchVision Object Detection Finetuning Tutorial Transfer Learning for Computer Vision Tutorial Adversarial Example Generation DCGAN Tutorial Spatial Tansformer Networks Tutorial Optimizaing Vision Transformer Model for Deployment Quantized Transfer Learning for Computer Vision Tutorial | 7    | ResNet50 网络进行图像分类 图像分类迁移学习 模型对抗攻击 生成式对抗网络 | 4    | 使用 LeNet 在 MNIST 数据集实现图像分类 使用卷积神经网络进行图像分类 基于图片相似度的图片搜索 基于 U-Net 卷积神经网络实现宠物图像分割 通过 OCR 实现验证码识别 通过 Sub-Pixel 实现图像超分辨率 人脸关键点检测 点云处理：实现 PointNet 点云分类 | 7      |
+| CV 领域相关                  | Basic image classification Convolutional Neural Network Image classification Transfer learning and fine-tuning Transfer learning with TF Hub Data Augmentaion Image segmentation Object detection with TF Hub Neural style transfer DeepDream DCGAN Pix2Pix CycleGAN Adversarial FGSM Intro to Autoencoders Variational Autoencoder | 16   | TorchVision Object Detection Finetuning Tutorial Transfer Learning for Computer Vision Tutorial Adversarial Example Generation DCGAN Tutorial Spatial Tansformer Networks Tutorial Optimizaing Vision Transformer Model for Deployment Quantized Transfer Learning for Computer Vision Tutorial | 7    | ResNet50 网络进行图像分类 图像分类迁移学习 模型对抗攻击 生成式对抗网络 | 4    | 使用 LeNet 在 MNIST 数据集实现图像分类 使用卷积神经网络进行图像分类 基于图片相似度的图片搜索 基于 U-Net 卷积神经网络实现宠物图像分割 通过 OCR 实现验证码识别 通过 Sub-Pixel 实现图像超分辨率 人脸关键点检测 点云处理：实现 PointNet 点云分类 | 7      |
 | NLP 领域相关                 | Basic text classification Text classification with TF Hub Word embeddings Word2Vec Text classification with an RNN classify Text with BERT Solve GLUE tasks using BERT on TPU Neural machine translation with attention Image captioning | 9    | Language Modeling with nn.Transformer and TorchText NLP From Scratch: Classifying Names with a Character-Level RNN NLP From Scratch: Generating Names with a Character-Level RNN NLP From Scratch: Translation with a Sequence to Sequence Network and Attention Text classification with the torchtext library Language Translation with nn.Transformer and torchtext Dynamic Quantization on an LSTM Word Language Model Dynamic Quantization on BERT | 8    | 使用 RNN 实现情感分类 LSTM+CRF 实现序列标注                  | 2    | 用 N-Gram 模型在莎士比亚文集中训练 word embedding IMDB 数据集使用 BOW 网络的文本分类 使用预训练的词向量完成文本分类任务 使用注意力机制的 LSTM 的机器翻译 使用序列到序列模型完成数字加法 | 5      |
 | 语音领域相关                 |                                                              |      | Audio I/O Audio Resampling Audio Data Augmentation Audio Feature Extractions Audio Feature Augmentation Audio Datasets Speech Recognition with Wav2Vec2 Speech Command Classification with torchaudio Text-to-speech with torchaudio Forced Alignment with Wav2Vec2 | 10   |                                                              | 0    |                                                              | 0      |
 | 推荐领域相关                 | Recommenders                                                 | 1    | Introduction to TorchRec                                     | 1    |                                                              | 0    | 使用协同过滤实现电影推荐                                     | 1      |

docs/guides/advanced/layer_and_model_en.md

@@ -11,7 +11,7 @@ In this guide, you will learn how to define and make use of models in Paddle, an
 
 In Paddle, most models consist of a series of layers. Layer serves as the foundamental logical unit of a model, composed of two parts: the variable that participates in the computation and the operator(s) that actually perform the execution.
 
-Contructing a model from scratch could be painful, with tons of nested codes to write and maintain. To make life easier, Paddle provides foundamental data structure ``paddle.nn.Layer`` to simplify the contruction of layer or model. One may easily inherit from ``paddle.nn.Layer`` to define thier custom layers or models. In addition, since both model and layer are essentially inherited from ``paddle.nn.Layer``, model is nothing but a special layer in Paddle.
+Constructing a model from scratch could be painful, with tons of nested codes to write and maintain. To make life easier, Paddle provides foundamental data structure ``paddle.nn.Layer`` to simplify the contruction of layer or model. One may easily inherit from ``paddle.nn.Layer`` to define thier custom layers or models. In addition, since both model and layer are essentially inherited from ``paddle.nn.Layer``, model is nothing but a special layer in Paddle.
 
 Now let us construct a model using ``paddle.nn.Layer``: