documentation: minor style changes to pass codebuild

Author: Raghav Dhall

doc/algorithms/other/index.rst

@@ -7,4 +7,4 @@ Other
 .. toctree::
     :maxdepth: 2
 
-    sagemaker.amazon.amazon_estimator
+    sagemaker.amazon.amazon_estimator

doc/algorithms/tabular/autogluon.rst

@@ -2,7 +2,7 @@
 AutoGluon
 ############
 
-`AutoGluon-Tabular <https://auto.gluon.ai/stable/index.html>`__ is a popular open-source AutoML framework that trains highly accurate machine learning models on an unprocessed tabular dataset. 
+`AutoGluon-Tabular <https://auto.gluon.ai/stable/index.html>`__ is a popular open-source AutoML framework that trains highly accurate machine learning models on an unprocessed tabular dataset.
 Unlike existing AutoML frameworks that primarily focus on model and hyperparameter selection, AutoGluon-Tabular succeeds by ensembling multiple models and stacking them in multiple layers.
 
 
@@ -20,9 +20,9 @@ The following table outlines a variety of sample notebooks that address differen
      - This notebook demonstrates the use of the Amazon SageMaker AutoGluon-Tabular algorithm to train and host a tabular regression model.
 
 
-For instructions on how to create and access Jupyter notebook instances that you can use to run the example in SageMaker, see 
-`Use Amazon SageMaker Notebook Instances <https://docs.aws.amazon.com/sagemaker/latest/dg/nbi.html>`__. After you have created a notebook 
-instance and opened it, choose the SageMaker Examples tab to see a list of all of the SageMaker samples. To open a notebook, choose its 
+For instructions on how to create and access Jupyter notebook instances that you can use to run the example in SageMaker, see
+`Use Amazon SageMaker Notebook Instances <https://docs.aws.amazon.com/sagemaker/latest/dg/nbi.html>`__. After you have created a notebook
+instance and opened it, choose the SageMaker Examples tab to see a list of all of the SageMaker samples. To open a notebook, choose its
 Use tab and choose Create copy.
 
 For detailed documentation, please refer to the `Sagemaker AutoGluon-Tabular Algorithm <https://docs.aws.amazon.com/sagemaker/latest/dg/autogluon-tabular.html>`__.

doc/algorithms/tabular/catboost.rst

@@ -3,8 +3,8 @@ CatBoost
 ############
 
 
-`CatBoost <https://catboost.ai/>`__ is a popular and high-performance open-source implementation of the Gradient Boosting Decision Tree (GBDT) 
-algorithm. GBDT is a supervised learning algorithm that attempts to accurately predict a target variable by combining an ensemble of 
+`CatBoost <https://catboost.ai/>`__ is a popular and high-performance open-source implementation of the Gradient Boosting Decision Tree (GBDT)
+algorithm. GBDT is a supervised learning algorithm that attempts to accurately predict a target variable by combining an ensemble of
 estimates from a set of simpler and weaker models.
 
 CatBoost introduces two critical algorithmic advances to GBDT:
@@ -13,7 +13,7 @@ CatBoost introduces two critical algorithmic advances to GBDT:
 
 * An innovative algorithm for processing categorical features
 
-Both techniques were created to fight a prediction shift caused by a special kind of target leakage present in all currently existing 
+Both techniques were created to fight a prediction shift caused by a special kind of target leakage present in all currently existing
 implementations of gradient boosting algorithms.
 
 The following table outlines a variety of sample notebooks that address different use cases of Amazon SageMaker CatBoost algorithm.
@@ -27,11 +27,11 @@ The following table outlines a variety of sample notebooks that address differen
    * - `Tabular classification with Amazon SageMaker LightGBM and CatBoost algorithm <https://github.com/aws/amazon-sagemaker-examples/blob/main/introduction_to_amazon_algorithms/lightgbm_catboost_tabular/Amazon_Tabular_Classification_LightGBM_CatBoost.ipynb>`__
      - This notebook demonstrates the use of the Amazon SageMaker CatBoost algorithm to train and host a tabular classification model. 
    * - `Tabular regression with Amazon SageMaker LightGBM and CatBoost algorithm <https://github.com/aws/amazon-sagemaker-examples/blob/main/introduction_to_amazon_algorithms/lightgbm_catboost_tabular/Amazon_Tabular_Regression_LightGBM_CatBoost.ipynb>`__
-     - This notebook demonstrates the use of the Amazon SageMaker CatBoost algorithm to train and host a tabular regression model. 
+     - This notebook demonstrates the use of the Amazon SageMaker CatBoost algorithm to train and host a tabular regression model.
 
-For instructions on how to create and access Jupyter notebook instances that you can use to run the example in SageMaker, see 
-`Use Amazon SageMaker Notebook Instances <https://docs.aws.amazon.com/sagemaker/latest/dg/nbi.html>`__. After you have created a notebook 
-instance and opened it, choose the SageMaker Examples tab to see a list of all of the SageMaker samples. To open a notebook, choose its 
+For instructions on how to create and access Jupyter notebook instances that you can use to run the example in SageMaker, see
+`Use Amazon SageMaker Notebook Instances <https://docs.aws.amazon.com/sagemaker/latest/dg/nbi.html>`__. After you have created a notebook
+instance and opened it, choose the SageMaker Examples tab to see a list of all of the SageMaker samples. To open a notebook, choose its
 Use tab and choose Create copy.
 
 For detailed documentation, please refer to the `Sagemaker CatBoost Algorithm <https://docs.aws.amazon.com/sagemaker/latest/dg/catboost.html>`__.

doc/algorithms/tabular/lightgbm.rst

@@ -2,10 +2,10 @@
 LightGBM
 ############
 
-`LightGBM <https://lightgbm.readthedocs.io/en/latest/>`__ is a popular and efficient open-source implementation of the Gradient Boosting 
-Decision Tree (GBDT) algorithm. GBDT is a supervised learning algorithm that attempts to accurately predict a target variable by 
-combining an ensemble of estimates from a set of simpler and weaker models. LightGBM uses additional techniques to significantly improve 
-the efficiency and scalability of conventional GBDT. 
+`LightGBM <https://lightgbm.readthedocs.io/en/latest/>`__ is a popular and efficient open-source implementation of the Gradient Boosting
+Decision Tree (GBDT) algorithm. GBDT is a supervised learning algorithm that attempts to accurately predict a target variable by
+combining an ensemble of estimates from a set of simpler and weaker models. LightGBM uses additional techniques to significantly improve
+the efficiency and scalability of conventional GBDT.
 
 The following table outlines a variety of sample notebooks that address different use cases of Amazon SageMaker LightGBM algorithm.
 
@@ -16,13 +16,13 @@ The following table outlines a variety of sample notebooks that address differen
    * - Notebook Title
      - Description
    * - `Tabular classification with Amazon SageMaker LightGBM and CatBoost algorithm <https://github.com/aws/amazon-sagemaker-examples/blob/main/introduction_to_amazon_algorithms/lightgbm_catboost_tabular/Amazon_Tabular_Classification_LightGBM_CatBoost.ipynb>`__
-     - This notebook demonstrates the use of the Amazon SageMaker LightGBM algorithm to train and host a tabular classification model. 
+     - This notebook demonstrates the use of the Amazon SageMaker LightGBM algorithm to train and host a tabular classification model.
    * - `Tabular regression with Amazon SageMaker LightGBM and CatBoost algorithm <https://github.com/aws/amazon-sagemaker-examples/blob/main/introduction_to_amazon_algorithms/lightgbm_catboost_tabular/Amazon_Tabular_Regression_LightGBM_CatBoost.ipynb>`__
-     - This notebook demonstrates the use of the Amazon SageMaker LightGBM algorithm to train and host a tabular regression model. 
+     - This notebook demonstrates the use of the Amazon SageMaker LightGBM algorithm to train and host a tabular regression model.
 
-For instructions on how to create and access Jupyter notebook instances that you can use to run the example in SageMaker, see 
-`Use Amazon SageMaker Notebook Instances <https://docs.aws.amazon.com/sagemaker/latest/dg/nbi.html>`__. After you have created a notebook 
-instance and opened it, choose the SageMaker Examples tab to see a list of all of the SageMaker samples. To open a notebook, choose its 
+For instructions on how to create and access Jupyter notebook instances that you can use to run the example in SageMaker, see
+`Use Amazon SageMaker Notebook Instances <https://docs.aws.amazon.com/sagemaker/latest/dg/nbi.html>`__. After you have created a notebook
+instance and opened it, choose the SageMaker Examples tab to see a list of all of the SageMaker samples. To open a notebook, choose its
 Use tab and choose Create copy.
 
 For detailed documentation, please refer to the `Sagemaker LightGBM Algorithm <https://docs.aws.amazon.com/sagemaker/latest/dg/lightgbm.html>`__.

doc/algorithms/tabular/tabtransformer.rst

@@ -2,8 +2,8 @@
 TabTransformer
 ###############
 
-`TabTransformer <https://arxiv.org/abs/2012.06678>`__ is a novel deep tabular data modeling architecture for supervised learning. The TabTransformer architecture is built on self-attention-based Transformers. 
-The Transformer layers transform the embeddings of categorical features into robust contextual embeddings to achieve higher prediction accuracy. Furthermore, the contextual embeddings learned from TabTransformer 
+`TabTransformer <https://arxiv.org/abs/2012.06678>`__ is a novel deep tabular data modeling architecture for supervised learning. The TabTransformer architecture is built on self-attention-based Transformers.
+The Transformer layers transform the embeddings of categorical features into robust contextual embeddings to achieve higher prediction accuracy. Furthermore, the contextual embeddings learned from TabTransformer
 are highly robust against both missing and noisy data features, and provide better interpretability.
 
 
@@ -16,13 +16,13 @@ The following table outlines a variety of sample notebooks that address differen
    * - Notebook Title
      - Description
    * - `Tabular classification with Amazon SageMaker TabTransformer algorithm <https://github.com/aws/amazon-sagemaker-examples/blob/main/introduction_to_amazon_algorithms/tabtransformer_tabular/Amazon_Tabular_Classification_TabTransformer.ipynb>`__
-     - This notebook demonstrates the use of the Amazon SageMaker TabTransformer algorithm to train and host a tabular classification model. 
+     - This notebook demonstrates the use of the Amazon SageMaker TabTransformer algorithm to train and host a tabular classification model.
    * - `Tabular regression with Amazon SageMaker TabTransformer algorithm <https://github.com/aws/amazon-sagemaker-examples/blob/main/introduction_to_amazon_algorithms/tabtransformer_tabular/Amazon_Tabular_Regression_TabTransformer.ipynb>`__
-     - This notebook demonstrates the use of the Amazon SageMaker TabTransformer algorithm to train and host a tabular regression model. 
+     - This notebook demonstrates the use of the Amazon SageMaker TabTransformer algorithm to train and host a tabular regression model.
 
-For instructions on how to create and access Jupyter notebook instances that you can use to run the example in SageMaker, see 
-`Use Amazon SageMaker Notebook Instances <https://docs.aws.amazon.com/sagemaker/latest/dg/nbi.html>`__. After you have created a notebook 
-instance and opened it, choose the SageMaker Examples tab to see a list of all of the SageMaker samples. To open a notebook, choose its 
+For instructions on how to create and access Jupyter notebook instances that you can use to run the example in SageMaker, see
+`Use Amazon SageMaker Notebook Instances <https://docs.aws.amazon.com/sagemaker/latest/dg/nbi.html>`__. After you have created a notebook
+instance and opened it, choose the SageMaker Examples tab to see a list of all of the SageMaker samples. To open a notebook, choose its
 Use tab and choose Create copy.
 
-For detailed documentation, please refer to the `Sagemaker TabTransformer Algorithm <https://docs.aws.amazon.com/sagemaker/latest/dg/tabtransformer.html>`__.
+For detailed documentation, please refer to the `Sagemaker TabTransformer Algorithm <https://docs.aws.amazon.com/sagemaker/latest/dg/tabtransformer.html>`__.

doc/algorithms/tabular/xgboost.rst

@@ -2,11 +2,11 @@
 XGBoost
 ############
 
-The `XGBoost <https://github.com/dmlc/xgboost>`__ (eXtreme Gradient Boosting) is a popular and efficient open-source implementation of the gradient boosted trees algorithm. Gradient boosting is a supervised learning algorithm that attempts to accurately predict a target variable 
-by combining an ensemble of estimates from a set of simpler and weaker models. The XGBoost algorithm performs well in machine learning competitions because of its robust handling of a variety of data types, relationships, distributions, and the variety of hyperparameters that you can 
+The `XGBoost <https://github.com/dmlc/xgboost>`__ (eXtreme Gradient Boosting) is a popular and efficient open-source implementation of the gradient boosted trees algorithm. Gradient boosting is a supervised learning algorithm that attempts to accurately predict a target variable
+by combining an ensemble of estimates from a set of simpler and weaker models. The XGBoost algorithm performs well in machine learning competitions because of its robust handling of a variety of data types, relationships, distributions, and the variety of hyperparameters that you can
 fine-tune. You can use XGBoost for regression, classification (binary and multiclass), and ranking problems.
 
-You can use the new release of the XGBoost algorithm either as a Amazon SageMaker built-in algorithm or as a framework to run training scripts in your local environments. This implementation has a smaller memory footprint, better logging, improved hyperparameter validation, and 
+You can use the new release of the XGBoost algorithm either as a Amazon SageMaker built-in algorithm or as a framework to run training scripts in your local environments. This implementation has a smaller memory footprint, better logging, improved hyperparameter validation, and
 an expanded set of metrics than the original versions. It provides an XGBoost estimator that executes a training script in a managed XGBoost environment. The current release of SageMaker XGBoost is based on the original XGBoost versions 1.0, 1.2, 1.3, and 1.5.
 
 The following table outlines a variety of sample notebooks that address different use cases of Amazon SageMaker XGBoost algorithm.
@@ -18,7 +18,7 @@ The following table outlines a variety of sample notebooks that address differen
    * - Notebook Title
      - Description
    * - `How to Create a Custom XGBoost container? <https://sagemaker-examples.readthedocs.io/en/latest/aws_sagemaker_studio/sagemaker_studio_image_build/xgboost_bring_your_own/Batch_Transform_BYO_XGB.html>`__
-     - This notebook shows you how to build a custom XGBoost Container with Amazon SageMaker Batch Transform. 
+     - This notebook shows you how to build a custom XGBoost Container with Amazon SageMaker Batch Transform.
    * - `Regression with XGBoost using Parquet <https://sagemaker-examples.readthedocs.io/en/latest/introduction_to_amazon_algorithms/xgboost_abalone/xgboost_parquet_input_training.html>`__
      - This notebook shows you how to use the Abalone dataset in Parquet to train a XGBoost model.
    * - `How to Train and Host a Multiclass Classification Model? <https://sagemaker-examples.readthedocs.io/en/latest/introduction_to_amazon_algorithms/xgboost_mnist/xgboost_mnist.html>`__
@@ -32,9 +32,9 @@ The following table outlines a variety of sample notebooks that address differen
    * - `How to use Amazon SageMaker Debugger to debug XGBoost Training Jobs in Real-Time? <https://sagemaker-examples.readthedocs.io/en/latest/sagemaker-debugger/xgboost_realtime_analysis/xgboost-realtime-analysis.html>`__
      - This notebook shows you how to use the MNIST dataset and Amazon SageMaker Debugger to perform real-time analysis of XGBoost training jobs while training jobs are running.
 
-For instructions on how to create and access Jupyter notebook instances that you can use to run the example in SageMaker, see 
-`Use Amazon SageMaker Notebook Instances <https://docs.aws.amazon.com/sagemaker/latest/dg/nbi.html>`__. After you have created a notebook 
-instance and opened it, choose the SageMaker Examples tab to see a list of all of the SageMaker samples. To open a notebook, choose its 
+For instructions on how to create and access Jupyter notebook instances that you can use to run the example in SageMaker, see
+`Use Amazon SageMaker Notebook Instances <https://docs.aws.amazon.com/sagemaker/latest/dg/nbi.html>`__. After you have created a notebook
+instance and opened it, choose the SageMaker Examples tab to see a list of all of the SageMaker samples. To open a notebook, choose its
 Use tab and choose Create copy.
 
 For detailed documentation, please refer to the `Sagemaker XGBoost Algorithm <https://docs.aws.amazon.com/sagemaker/latest/dg/xgboost.html>`__.