Fix tutorials/*/README.md

fix references to notebook files and typos

ref:6cd192a4df1e49a2f86ea0f0c43c99e5d21adb48

Author: dbakshee

README.md

@@ -37,7 +37,7 @@ It's better to start CatBoost exploring from this basic tutorials.
 ## Model analysis
 
 * [Object Importance Tutorial](model_analysis/object_importance_tutorial.ipynb)
-    * This tutorial shows how to evaluate importances of the train objects for test objects. And with using of importance scores detect noisy train objects.
+    * This tutorial shows how to evaluate importances of the train objects for test objects, and how to detect broken train objects by using the importance scores.
 
 * [SHAP Values Tutorial](model_analysis/shap_values_tutorial.ipynb)
     * This tutorial shows how to use [SHAP](https://github.com/slundberg/shap) python-package to get and visualize feature importances.
@@ -49,7 +49,7 @@ It's better to start CatBoost exploring from this basic tutorials.
     * This tutorial shows how to visualize catboost decision trees.
 
 * [Feature statistics tutorial](model_analysis/feature_statistics_tutorial.ipynb)
-    * This tutorial shows how to calculate feature statistics for catboost model
+    * This tutorial shows how to calculate feature statistics for catboost model.
 
 ## Custom loss
 
@@ -76,7 +76,7 @@ It's better to start CatBoost exploring from this basic tutorials.
 ## Tools
 
 * [Gradient Boosting: CPU vs GPU](tools/google_colaboratory_cpu_vs_gpu_tutorial.ipynb)
-    * This is a basic tutorial which shows how to run gradient boosting on CPU and GPU on Google Colaboratory. 
+    * This is a basic tutorial which shows how to run gradient boosting on CPU and GPU on Google Colaboratory.
 
 * [Regression on Gradient Boosting: CPU vs GPU](tools/google_colaboratory_cpu_vs_gpu_regression_tutorial.ipynb)
     * This is a basic tutorial which shows how to run regression on gradient boosting on CPU and GPU on Google Colaboratory.
@@ -112,6 +112,6 @@ It's better to start CatBoost exploring from this basic tutorials.
 * [PyData Boston tutorial](events/2019_odsc_east/odsc_east_2019.ipynb)
     * Tutorial from PyData Boston, April 30, 2019.
 
-## Tutorials on Russian
+## Tutorials in Russian
 
-* Find tutorials on Russian language on the separate [page](ru/README.md).
+* Find tutorials in Russian on the separate [page](ru/README.md).

apply_model/README.md

@@ -1,16 +1,16 @@
 ## Apply model
 
-* [CatBoost CoreML Tutorial](apply_model/coreml/coreml_export_tutorial.ipynb)
+* [CatBoost CoreML Tutorial](./coreml/coreml_export_tutorial.ipynb)
     * Explore this tutorial to learn how to convert CatBoost model to CoreML format and use it on any iOS device.
 
-* [Export CatBoost Model as C++ code Tutorial](apply_model/model_export_as_cpp_code_tutorial.md)
+* [Export CatBoost Model as C++ code Tutorial](./model_export_as_cpp_code_tutorial.md)
     * Catboost model could be saved as standalone C++ code.
 
-* [Export CatBoost Model as Python code Tutorial](apply_model/model_export_as_python_code_tutorial.md)
+* [Export CatBoost Model as Python code Tutorial](./model_export_as_python_code_tutorial.md)
     * Catboost model could be saved as standalone Python code.
 
-* [Apply CatBoost model from Java](apply_model/java/train_model.ipynb)
-    * Explore how to apply CatBoost model from Java application. If you just want to look at code snippets you can go directly to [CatBoost4jPredictionTutorial.java](apply_model/java/src/main/java/CatBoost4jPredictionTutorial.java)
+* [Apply CatBoost model from Java](./java/train_model.ipynb)
+    * Explore how to apply CatBoost model from Java application. If you just want to look at code snippets you can go directly to [CatBoost4jPredictionTutorial.java](./java/src/main/java/CatBoost4jPredictionTutorial.java)
 
-* [Apply CatBoost model from Rust](apply_model/rust/train_model.ipynb)
-    * Explore how to apply CatBoost model from Rust application. If you just want to look at code snippets you can go directly to [main.rs](apply_model/rust/src/main.rs)
+* [Apply CatBoost model from Rust](./rust/train_model.ipynb)
+    * Explore how to apply CatBoost model from Rust application. If you just want to look at code snippets you can go directly to [main.rs](./rust/src/main.rs)

apply_model/dotnet/README.md

@@ -1 +1 @@
-Tutorial for applying theo model in .NET
+Tutorial for applying the model in .NET

events/README.md

@@ -1,19 +1,19 @@
 ## Events
 
-* [PyData Moscow tutorial](events/pydata_moscow_oct_13_2018.ipynb)
+* [PyData Moscow tutorial](./pydata_moscow_oct_13_2018.ipynb)
     * Tutorial from PyData Moscow, October 13, 2018.
 
-* [PyData NYC tutorial](events/pydata_nyc_oct_19_2018.ipynb)
+* [PyData NYC tutorial](./pydata_nyc_oct_19_2018.ipynb)
     * Tutorial from PyData New York, October 19, 2018.
 
-* [PyData LA tutorial](events/pydata_la_oct_21_2018.ipynb)
+* [PyData LA tutorial](./pydata_la_oct_21_2018.ipynb)
     * Tutorial from PyData Los Angeles, October 21, 2018.
 
-* [PyData Moscow tutorial](events/datastart_moscow_apr_27_2019.ipynb)
+* [PyData Moscow tutorial](./datastart_moscow_apr_27_2019.ipynb)
     * Tutorial from PyData Moscow, April 27, 2019.
 
-* [PyData London tutorial](events/2019_pydata_london/pydata_london_2019.ipynb)
+* [PyData London tutorial](./2019_pydata_london/pydata_london_2019.ipynb)
     * Tutorial from PyData London, June 15, 2019.
 
-* [PyData Boston tutorial](events/2019_odsc_east/odsc_east_2019.ipynb)
+* [PyData Boston tutorial](./2019_odsc_east/odsc_east_2019.ipynb)
     * Tutorial from PyData Boston, April 30, 2019.

model_analysis/README.md

@@ -1,16 +1,16 @@
 ## Model analysis
 
-* [Object Importance Tutorial](model_analysis/object_importance_tutorial.ipynb)
-    * This tutorial shows how to evaluate importances of the train objects for test objects. And with using of importance scores detect noisy train objects.
+* [Object Importance Tutorial](./object_importance_tutorial.ipynb)
+    * This tutorial shows how to evaluate importances of the train objects for test objects, and how to detect noisy train objects by using the importance scores.
 
-* [SHAP Values Tutorial](model_analysis/shap_values_tutorial.ipynb)
+* [SHAP Values Tutorial](./shap_values_tutorial.ipynb)
     * This tutorial shows how to use [SHAP](https://github.com/slundberg/shap) python-package to get and visualize feature importances.
 
-* [Export CatBoost Model in JSON format Tutorial](model_analysis/model_export_as_json_tutorial.ipynb)
+* [Export CatBoost Model in JSON format Tutorial](./model_export_as_json_tutorial.ipynb)
     * This tutorial shows how to save catboost model in JSON format and apply it.
 
-* [Visualization of CatBoost decision trees tutorial](model_analysis/visualize_decision_trees_tutorial.ipynb)
+* [Visualization of CatBoost decision trees tutorial](./visualize_decision_trees_tutorial.ipynb)
     * This tutorial shows how to visualize catboost decision trees.
 
-* [Feature statistics tutorial](model_analysis/feature_statistics_tutorial.ipynb)
-    * This tutorial shows how to calculate feature statistics for catboost model
+* [Feature statistics tutorial](./feature_statistics_tutorial.ipynb)
+    * This tutorial shows how to calculate feature statistics for catboost model.

tools/README.md

@@ -1,7 +1,7 @@
 ## Tools
 
-* [Gradient Boosting: CPU vs GPU](tools/google_colaboratory_cpu_vs_gpu_tutorial.ipynb)
-    * This is a basic tutorial which shows how to run gradient boosting on CPU and GPU on Google Colaboratory. 
+* [Gradient Boosting: CPU vs GPU](./google_colaboratory_cpu_vs_gpu_tutorial.ipynb)
+    * This is a basic tutorial which shows how to run gradient boosting on CPU and GPU on Google Colaboratory.
 
-* [Regression on Gradient Boosting: CPU vs GPU](tools/google_colaboratory_cpu_vs_gpu_regression_tutorial.ipynb)
+* [Regression on Gradient Boosting: CPU vs GPU](./google_colaboratory_cpu_vs_gpu_regression_tutorial.ipynb)
     * This is a basic tutorial which shows how to run regression on gradient boosting on CPU and GPU on Google Colaboratory.