From 6d9ca704014cef073778772054121b48fcdff66b Mon Sep 17 00:00:00 2001
From: Julia Muiruri <julia.muiruri4@gmail.com>
Date: Mon, 18 Jul 2022 13:55:45 +0300
Subject: [PATCH] added links to the new quiz apps
---
1-Introduction/1-intro-to-ML/README.md | 4 ++--
1-Introduction/1-intro-to-ML/translations/README.bn.md | 4 ++--
1-Introduction/1-intro-to-ML/translations/README.es.md | 4 ++--
1-Introduction/1-intro-to-ML/translations/README.fr.md | 4 ++--
1-Introduction/1-intro-to-ML/translations/README.id.md | 4 ++--
1-Introduction/1-intro-to-ML/translations/README.it.md | 4 ++--
1-Introduction/1-intro-to-ML/translations/README.ja.md | 4 ++--
1-Introduction/1-intro-to-ML/translations/README.ko.md | 4 ++--
1-Introduction/1-intro-to-ML/translations/README.pt-br.md | 4 ++--
1-Introduction/1-intro-to-ML/translations/README.ru.md | 4 ++--
1-Introduction/1-intro-to-ML/translations/README.tr.md | 4 ++--
1-Introduction/1-intro-to-ML/translations/README.zh-cn.md | 4 ++--
1-Introduction/1-intro-to-ML/translations/README.zh-tw.md | 4 ++--
1-Introduction/2-history-of-ML/README.md | 4 ++--
1-Introduction/2-history-of-ML/translations/README.es.md | 4 ++--
1-Introduction/2-history-of-ML/translations/README.fr.md | 4 ++--
1-Introduction/2-history-of-ML/translations/README.id.md | 4 ++--
1-Introduction/2-history-of-ML/translations/README.it.md | 4 ++--
1-Introduction/2-history-of-ML/translations/README.ja.md | 4 ++--
1-Introduction/2-history-of-ML/translations/README.ko.md | 4 ++--
1-Introduction/2-history-of-ML/translations/README.pt-br.md | 4 ++--
1-Introduction/2-history-of-ML/translations/README.ru.md | 4 ++--
1-Introduction/2-history-of-ML/translations/README.tr.md | 4 ++--
1-Introduction/2-history-of-ML/translations/README.zh-cn.md | 4 ++--
1-Introduction/2-history-of-ML/translations/README.zh-tw.md | 4 ++--
1-Introduction/3-fairness/README.md | 4 ++--
1-Introduction/3-fairness/translations/README.es.md | 4 ++--
1-Introduction/3-fairness/translations/README.fr.md | 4 ++--
1-Introduction/3-fairness/translations/README.id.md | 4 ++--
1-Introduction/3-fairness/translations/README.it.md | 4 ++--
1-Introduction/3-fairness/translations/README.ja.md | 4 ++--
1-Introduction/3-fairness/translations/README.ko.md | 4 ++--
1-Introduction/3-fairness/translations/README.pt-br.md | 4 ++--
1-Introduction/3-fairness/translations/README.zh-cn.md | 4 ++--
1-Introduction/3-fairness/translations/README.zh-tw.md | 4 ++--
1-Introduction/4-techniques-of-ML/README.md | 4 ++--
1-Introduction/4-techniques-of-ML/translations/README.es.md | 4 ++--
1-Introduction/4-techniques-of-ML/translations/README.id.md | 4 ++--
1-Introduction/4-techniques-of-ML/translations/README.it.md | 4 ++--
1-Introduction/4-techniques-of-ML/translations/README.ja.md | 4 ++--
1-Introduction/4-techniques-of-ML/translations/README.ko.md | 4 ++--
.../4-techniques-of-ML/translations/README.pt-br.md | 4 ++--
.../4-techniques-of-ML/translations/README.zh-cn.md | 4 ++--
.../4-techniques-of-ML/translations/README.zh-tw.md | 4 ++--
2-Regression/1-Tools/README.md | 4 ++--
2-Regression/1-Tools/translations/README.id.md | 4 ++--
2-Regression/1-Tools/translations/README.it.md | 4 ++--
2-Regression/1-Tools/translations/README.ja.md | 4 ++--
2-Regression/1-Tools/translations/README.ko.md | 4 ++--
2-Regression/1-Tools/translations/README.pt-br.md | 4 ++--
2-Regression/1-Tools/translations/README.pt.md | 4 ++--
2-Regression/1-Tools/translations/README.tr.md | 4 ++--
2-Regression/1-Tools/translations/README.zh-cn.md | 4 ++--
2-Regression/1-Tools/translations/README.zh-tw.md | 4 ++--
2-Regression/2-Data/README.md | 4 ++--
2-Regression/2-Data/translations/README.es.md | 4 ++--
2-Regression/2-Data/translations/README.id.md | 4 ++--
2-Regression/2-Data/translations/README.it.md | 4 ++--
2-Regression/2-Data/translations/README.ja.md | 4 ++--
2-Regression/2-Data/translations/README.ko.md | 4 ++--
2-Regression/2-Data/translations/README.pt-br.md | 4 ++--
2-Regression/2-Data/translations/README.pt.md | 4 ++--
2-Regression/2-Data/translations/README.zh-cn.md | 4 ++--
2-Regression/2-Data/translations/README.zh-tw.md | 4 ++--
2-Regression/3-Linear/README.md | 4 ++--
2-Regression/3-Linear/solution/R/lesson_3-R.ipynb | 2 +-
2-Regression/3-Linear/solution/R/lesson_3.Rmd | 2 +-
2-Regression/3-Linear/translations/README.es.md | 4 ++--
2-Regression/3-Linear/translations/README.id.md | 4 ++--
2-Regression/3-Linear/translations/README.it.md | 4 ++--
2-Regression/3-Linear/translations/README.ja.md | 4 ++--
2-Regression/3-Linear/translations/README.ko.md | 4 ++--
2-Regression/3-Linear/translations/README.pt-br.md | 4 ++--
2-Regression/3-Linear/translations/README.pt.md | 4 ++--
2-Regression/3-Linear/translations/README.zh-cn.md | 4 ++--
2-Regression/3-Linear/translations/README.zh-tw.md | 4 ++--
2-Regression/4-Logistic/README.md | 4 ++--
2-Regression/4-Logistic/solution/R/lesson_4-R.ipynb | 2 +-
2-Regression/4-Logistic/solution/R/lesson_4.Rmd | 2 +-
2-Regression/4-Logistic/translations/README.es.md | 4 ++--
2-Regression/4-Logistic/translations/README.id.md | 4 ++--
2-Regression/4-Logistic/translations/README.it.md | 4 ++--
2-Regression/4-Logistic/translations/README.ja.md | 4 ++--
2-Regression/4-Logistic/translations/README.ko.md | 4 ++--
2-Regression/4-Logistic/translations/README.pt-br.md | 4 ++--
2-Regression/4-Logistic/translations/README.pt.md | 4 ++--
2-Regression/4-Logistic/translations/README.zh-cn.md | 4 ++--
2-Regression/4-Logistic/translations/README.zh-tw.md | 4 ++--
3-Web-App/1-Web-App/README.md | 4 ++--
3-Web-App/1-Web-App/translations/README.es.md | 4 ++--
3-Web-App/1-Web-App/translations/README.it.md | 4 ++--
3-Web-App/1-Web-App/translations/README.ja.md | 4 ++--
3-Web-App/1-Web-App/translations/README.ko.md | 4 ++--
3-Web-App/1-Web-App/translations/README.pt-br.md | 4 ++--
3-Web-App/1-Web-App/translations/README.pt.md | 4 ++--
3-Web-App/1-Web-App/translations/README.zh-cn.md | 4 ++--
4-Classification/1-Introduction/README.md | 4 ++--
4-Classification/1-Introduction/solution/R/lesson_10-R.ipynb | 4 ++--
4-Classification/1-Introduction/solution/R/lesson_10.Rmd | 4 ++--
4-Classification/1-Introduction/translations/README.es.md | 4 ++--
4-Classification/1-Introduction/translations/README.it.md | 4 ++--
4-Classification/1-Introduction/translations/README.ko.md | 4 ++--
4-Classification/1-Introduction/translations/README.pt-br.md | 4 ++--
4-Classification/1-Introduction/translations/README.tr.md | 4 ++--
4-Classification/1-Introduction/translations/README.zh-cn.md | 4 ++--
4-Classification/2-Classifiers-1/README.md | 4 ++--
4-Classification/2-Classifiers-1/solution/R/lesson_11-R.ipynb | 2 +-
4-Classification/2-Classifiers-1/solution/R/lesson_11.Rmd | 2 +-
4-Classification/2-Classifiers-1/translations/README.es.md | 4 ++--
4-Classification/2-Classifiers-1/translations/README.it.md | 4 ++--
4-Classification/2-Classifiers-1/translations/README.ko.md | 4 ++--
4-Classification/2-Classifiers-1/translations/README.pt-br.md | 4 ++--
4-Classification/2-Classifiers-1/translations/README.tr.md | 4 ++--
4-Classification/2-Classifiers-1/translations/README.zh-cn.md | 4 ++--
4-Classification/3-Classifiers-2/README.md | 4 ++--
4-Classification/3-Classifiers-2/solution/R/lesson_12-R.ipynb | 4 ++--
4-Classification/3-Classifiers-2/solution/R/lesson_12.Rmd | 4 ++--
4-Classification/3-Classifiers-2/translations/README.es.md | 4 ++--
4-Classification/3-Classifiers-2/translations/README.it.md | 4 ++--
4-Classification/3-Classifiers-2/translations/README.ko.md | 4 ++--
4-Classification/3-Classifiers-2/translations/README.pt-br.md | 4 ++--
4-Classification/3-Classifiers-2/translations/README.tr.md | 4 ++--
4-Classification/3-Classifiers-2/translations/README.zh-cn.md | 4 ++--
4-Classification/4-Applied/README.md | 4 ++--
4-Classification/4-Applied/translations/README.es.md | 4 ++--
4-Classification/4-Applied/translations/README.it.md | 4 ++--
4-Classification/4-Applied/translations/README.ko.md | 4 ++--
4-Classification/4-Applied/translations/README.pt-br.md | 4 ++--
4-Classification/4-Applied/translations/README.tr.md | 4 ++--
4-Classification/4-Applied/translations/README.zh-CN.md | 4 ++--
5-Clustering/1-Visualize/README.md | 4 ++--
5-Clustering/1-Visualize/solution/R/lesson_14-R.ipynb | 4 ++--
5-Clustering/1-Visualize/solution/R/lesson_14.Rmd | 4 ++--
5-Clustering/1-Visualize/translations/README.es.md | 4 ++--
5-Clustering/1-Visualize/translations/README.it.md | 4 ++--
5-Clustering/1-Visualize/translations/README.ko.md | 4 ++--
5-Clustering/1-Visualize/translations/README.zh-cn.md | 4 ++--
5-Clustering/2-K-Means/README.md | 4 ++--
5-Clustering/2-K-Means/solution/R/lesson_15-R.ipynb | 4 ++--
5-Clustering/2-K-Means/solution/R/lesson_15.Rmd | 4 ++--
5-Clustering/2-K-Means/translations/README.es.md | 4 ++--
5-Clustering/2-K-Means/translations/README.it.md | 4 ++--
5-Clustering/2-K-Means/translations/README.ko.md | 4 ++--
5-Clustering/2-K-Means/translations/README.zh-cn.md | 4 ++--
6-NLP/1-Introduction-to-NLP/README.md | 4 ++--
6-NLP/1-Introduction-to-NLP/translations/README.es.md | 4 ++--
6-NLP/1-Introduction-to-NLP/translations/README.it.md | 4 ++--
6-NLP/1-Introduction-to-NLP/translations/README.ko.md | 4 ++--
6-NLP/1-Introduction-to-NLP/translations/README.pt-br.md | 4 ++--
6-NLP/1-Introduction-to-NLP/translations/README.zh-cn.md | 4 ++--
6-NLP/2-Tasks/README.md | 4 ++--
6-NLP/2-Tasks/translations/README.es.md | 4 ++--
6-NLP/2-Tasks/translations/README.it.md | 4 ++--
6-NLP/2-Tasks/translations/README.ko.md | 4 ++--
6-NLP/2-Tasks/translations/README.pt-br.md | 4 ++--
6-NLP/3-Translation-Sentiment/README.md | 4 ++--
6-NLP/3-Translation-Sentiment/translations/README.es.md | 4 ++--
6-NLP/3-Translation-Sentiment/translations/README.it.md | 4 ++--
6-NLP/3-Translation-Sentiment/translations/README.ko.md | 4 ++--
6-NLP/4-Hotel-Reviews-1/README.md | 4 ++--
6-NLP/4-Hotel-Reviews-1/translations/README.es.md | 4 ++--
6-NLP/4-Hotel-Reviews-1/translations/README.it.md | 4 ++--
6-NLP/4-Hotel-Reviews-1/translations/README.ko.md | 4 ++--
6-NLP/5-Hotel-Reviews-2/README.md | 4 ++--
6-NLP/5-Hotel-Reviews-2/translations/README.es.md | 4 ++--
6-NLP/5-Hotel-Reviews-2/translations/README.it.md | 4 ++--
6-NLP/5-Hotel-Reviews-2/translations/README.ko.md | 4 ++--
7-TimeSeries/1-Introduction/README.md | 4 ++--
7-TimeSeries/1-Introduction/translations/README.es.md | 4 ++--
7-TimeSeries/1-Introduction/translations/README.it.md | 4 ++--
7-TimeSeries/1-Introduction/translations/README.ko.md | 4 ++--
7-TimeSeries/2-ARIMA/README.md | 4 ++--
7-TimeSeries/2-ARIMA/translations/README.it.md | 4 ++--
7-TimeSeries/2-ARIMA/translations/README.ko.md | 4 ++--
7-TimeSeries/3-SVR/README.md | 4 ++--
8-Reinforcement/1-QLearning/README.md | 4 ++--
8-Reinforcement/1-QLearning/translations/README.it.md | 4 ++--
8-Reinforcement/1-QLearning/translations/README.ko.md | 4 ++--
8-Reinforcement/1-QLearning/translations/README.zh-cn.md | 4 ++--
8-Reinforcement/2-Gym/README.md | 4 ++--
8-Reinforcement/2-Gym/translations/README.it.md | 4 ++--
8-Reinforcement/2-Gym/translations/README.ko.md | 4 ++--
8-Reinforcement/2-Gym/translations/README.zh-cn.md | 4 ++--
9-Real-World/1-Applications/README.md | 4 ++--
9-Real-World/1-Applications/translations/README.it.md | 4 ++--
9-Real-World/1-Applications/translations/README.ko.md | 4 ++--
README.md | 2 +-
TRANSLATIONS.md | 2 +-
translations/README.es.md | 2 +-
translations/README.hi.md | 2 +-
translations/README.it.md | 2 +-
translations/README.ja.md | 2 +-
translations/README.ko.md | 2 +-
translations/README.ms.md | 2 +-
translations/README.pt-br.md | 2 +-
translations/README.pt.md | 2 +-
translations/README.tr.md | 2 +-
translations/README.zh-cn.md | 2 +-
translations/Readme.ta.md | 2 +-
199 files changed, 379 insertions(+), 379 deletions(-)
diff --git a/1-Introduction/1-intro-to-ML/README.md b/1-Introduction/1-intro-to-ML/README.md
index 36ee822c..55381707 100644
--- a/1-Introduction/1-intro-to-ML/README.md
+++ b/1-Introduction/1-intro-to-ML/README.md
@@ -8,7 +8,7 @@ Watch the video, then take the pre-lesson quiz
> 馃帴 Click the image above for a video discussing the difference between machine learning, AI, and deep learning.
-## [Pre-lecture quiz](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/1/)
+## [Pre-lecture quiz](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/1/)
---
@@ -134,7 +134,7 @@ In the near future, understanding the basics of machine learning is going to be
Sketch, on paper or using an online app like [Excalidraw](https://excalidraw.com/), your understanding of the differences between AI, ML, deep learning, and data science. Add some ideas of problems that each of these techniques are good at solving.
-# [Post-lecture quiz](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/2/)
+# [Post-lecture quiz](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/2/)
---
# Review & Self Study
diff --git a/1-Introduction/1-intro-to-ML/translations/README.bn.md b/1-Introduction/1-intro-to-ML/translations/README.bn.md
index 3eaed0fe..66a4b3b7 100644
--- a/1-Introduction/1-intro-to-ML/translations/README.bn.md
+++ b/1-Introduction/1-intro-to-ML/translations/README.bn.md
@@ -7,7 +7,7 @@ Watch the video, then take the pre-lesson quiz
> 馃帴 唳唳多唳� 唳侧唳班唳ㄠ唳�, 唳忇唳�(唳嗋Π唰嵿唳苦Λ唳苦Χ唳苦唳距Σ 唳囙Θ唰嵿唳苦Σ唳苦唰囙Θ唰嵿Ω) 唳忇Μ唳� 唳∴唳� 唳侧唳班唳ㄠ唳� 唳忇Π 唳Η唰嵿Ο唰� 唳唳班唳ム唰嵿Ο 唳忇Π 唳嗋Σ唰嬥唳ㄠ 唳溹唳ㄠΔ唰� 唳夃Κ唳班唳� 唳涏Μ唳苦唳苦Δ唰� 唳曕唳侧唳� 唳曕Π唰� 唳唳∴唳撪唳� 唳︵唳栢唳ㄠイ
-## [唳唳班-唳侧唳曕唳距Π-唳曕唳囙](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/1/)
+## [唳唳班-唳侧唳曕唳距Π-唳曕唳囙](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/1/)
---
唳唳椸唳ㄠ唳班Ζ唰囙Π 唳溹Θ唰嵿Ο 唳曕唳侧唳膏唳曕唳唳� 唳唳多唳� 唳侧唳班唳ㄠ唳� 唳曕唳距Π唰嵿Ω 唳� 唳嗋Κ唳ㄠ唳曕 唳膏唳唳椸Δ唳�!唳嗋Κ唳ㄠ 唳灌 唳忇 唳唳粪唰� 唳膏Ξ唰嵿Κ唰傕Π唰嵿Γ 唳ㄠΔ唰佮Θ 唳呧Ε唳 唳唳多唳� 唳侧唳班唳ㄠ唳� 唳� 唳ㄠ唳溹唳� 唳呧Θ唰佮Χ唰€唳侧Θ唳曕 唳嗋Π唳� 唳夃Θ唰嵿Θ唳� 唳曕Π唳む 唳氞唳�, 唳嗋Κ唳ㄠ 唳嗋Ξ唳距Ζ唰囙Π 唳膏唳ム 唳唳椸Ζ唳距Θ 唳曕Π唳む 唳唳班 唳嗋Ξ唳班 唳栢唳多! 唳嗋Ξ唳班 唳嗋Κ唳ㄠ唳� ML 唳呧Η唰嵿Ο唳唳ㄠ唳� 唳溹Θ唰嵿Ο 唳忇唳熰 唳Θ唰嵿Η唰佮Δ唰嵿Μ唳唳班唳� 唳侧唰嵿唳苦 唳膏唳 唳む唳班 唳曕Π唳む 唳氞唳� 唳忇Μ唳� 唳嗋Κ唳ㄠ唳� 唳唳侧唳唳唳�, 唳唳班Δ唳苦唰嵿Π唳苦Ο唳监,[唳唳∴Μ唰嵿Ο唳距](https://github.com/microsoft/ML-For-Beginners/discussions). 唳溹唳ㄠ唳む 唳忇Μ唳� 唳呧Θ唰嵿Δ唳班唳唳曕唳� 唳曕Π唳む 唳唳班 唳栢唳多 唳灌Μ 啷�
@@ -136,7 +136,7 @@ MIT 唳忇Π 唳溹Θ 唳椸唳熰唳� 唳唳多唳� 唳侧唳班唳ㄠ唳� 唳忇Π
唳膏唳曕唳�, 唳曕唳椸唰� 唳 唳忇唳熰 唳呧Θ唳侧唳囙Θ 唳呧唳唳� 唳唳Μ唳灌唳� 唳曕Π唰� [唳忇唰嵿Ω唳距Σ唳苦Α唰嵿Π](https://excalidraw.com/) AI, ML, 唳∴唳� 唳侧唳班唳ㄠ唳� 唳忇Μ唳� 唳∴唳熰 唳膏唳唰囙Θ唰嵿Ω唰囙Π 唳Η唰嵿Ο唰� 唳唳班唳ム唰嵿Ο 唳膏Ξ唰嵿Κ唳班唳曕啷�
-# [唳侧唳曕唳距Π-唳曕唳囙](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/2/)
+# [唳侧唳曕唳距Π-唳曕唳囙](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/2/)
---
# 唳Π唰嵿Ο唳距Σ唰嬥唳ㄠ 唳� 唳膏唳侧唳� 唳膏唳熰唳∴
diff --git a/1-Introduction/1-intro-to-ML/translations/README.es.md b/1-Introduction/1-intro-to-ML/translations/README.es.md
index fd193a0d..c22b6483 100644
--- a/1-Introduction/1-intro-to-ML/translations/README.es.md
+++ b/1-Introduction/1-intro-to-ML/translations/README.es.md
@@ -4,7 +4,7 @@
> 馃帴 Haz clic en la imagen de arriba para ver un video donde se discuten las diferencias entre el machine learning, la inteligencia artificial, y el deep learning.
-## [Cuestionario previo a la conferencia](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/1?loc=es)
+## [Cuestionario previo a la conferencia](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/1?loc=es)
### Introducci贸n
@@ -100,7 +100,7 @@ En el futuro pr贸ximo, entender las bases de machine learning va a ser una neces
Dibuja, en papel o usando una aplicaci贸n como [Excalidraw](https://excalidraw.com/), c贸mo entiendes las diferencias entre inteligencia artificial, ML, deep learning, y la ciencia de datos. Agrega algunas ideas de problemas que cada una de estas t茅cnicas son buenas en resolver.
-## [Cuestionario despu茅s de la lecci贸n](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/2?loc=es)
+## [Cuestionario despu茅s de la lecci贸n](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/2?loc=es)
## Revisi贸n y autoestudio
diff --git a/1-Introduction/1-intro-to-ML/translations/README.fr.md b/1-Introduction/1-intro-to-ML/translations/README.fr.md
index 717fedf9..6c735191 100644
--- a/1-Introduction/1-intro-to-ML/translations/README.fr.md
+++ b/1-Introduction/1-intro-to-ML/translations/README.fr.md
@@ -4,7 +4,7 @@
> 馃帴 Cliquer sur l'image ci-dessus afin de regarder une vid茅o expliquant la diff茅rence entre machine learning, AI et deep learning.
-## [Quiz pr茅alable](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/1?loc=fr)
+## [Quiz pr茅alable](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/1?loc=fr)
### Introduction
@@ -98,7 +98,7 @@ Dans un avenir proche, comprendre les bases du machine learning sera indispensab
Esquisser, sur papier ou 脿 l'aide d'une application en ligne comme [Excalidraw](https://excalidraw.com/), votre compr茅hension des diff茅rences entre l'IA, le ML, le deep learning et la data science. Ajouter quelques id茅es de probl猫mes que chacune de ces techniques est bonne 脿 r茅soudre.
-## [Quiz de validation des connaissances](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/2?loc=fr)
+## [Quiz de validation des connaissances](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/2?loc=fr)
## R茅vision et auto-apprentissage
diff --git a/1-Introduction/1-intro-to-ML/translations/README.id.md b/1-Introduction/1-intro-to-ML/translations/README.id.md
index 623e9fd1..44d30d6c 100644
--- a/1-Introduction/1-intro-to-ML/translations/README.id.md
+++ b/1-Introduction/1-intro-to-ML/translations/README.id.md
@@ -4,7 +4,7 @@
> 馃帴 Klik gambar diatas untuk menonton video yang mendiskusikan perbedaan antara Machine Learning, AI, dan Deep Learning.
-## [Quiz Pra-Pelajaran](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/1/)
+## [Quiz Pra-Pelajaran](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/1/)
### Pengantar
@@ -96,7 +96,7 @@ Dalam waktu dekat, memahami dasar-dasar Machine Learning akan menjadi suatu keha
Buat sketsa di atas kertas atau menggunakan aplikasi seperti [Excalidraw](https://excalidraw.com/), mengenai pemahaman kamu tentang perbedaan antara AI, ML, Deep Learning, dan Data Science. Tambahkan beberapa ide masalah yang cocok diselesaikan masing-masing teknik.
-## [Quiz Pasca-Pelajaran](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/2/)
+## [Quiz Pasca-Pelajaran](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/2/)
## Ulasan & Belajar Mandiri
diff --git a/1-Introduction/1-intro-to-ML/translations/README.it.md b/1-Introduction/1-intro-to-ML/translations/README.it.md
index b7806dcb..258f7882 100644
--- a/1-Introduction/1-intro-to-ML/translations/README.it.md
+++ b/1-Introduction/1-intro-to-ML/translations/README.it.md
@@ -4,7 +4,7 @@
> 馃帴 Fare clic sull'immagine sopra per un video che illustra la differenza tra machine learning, intelligenza artificiale (AI) e deep learning.
-## [Quiz pre-lezione](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/1/?loc=it)
+## [Quiz pre-lezione](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/1/?loc=it)
### Introduzione
@@ -97,7 +97,7 @@ Nel prossimo futuro, comprendere le basi di machine learning sar脿 un must per l
Disegnare, su carta o utilizzando un'app online come [Excalidraw](https://excalidraw.com/), la propria comprensione delle differenze tra AI, ML, deep learning e data science. Aggiungere alcune idee sui problemi che ciascuna di queste tecniche 猫 in grado di risolvere.
-## [Quiz post-lezione](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/2/?loc=it)
+## [Quiz post-lezione](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/2/?loc=it)
## Revisione e Auto Apprendimento
diff --git a/1-Introduction/1-intro-to-ML/translations/README.ja.md b/1-Introduction/1-intro-to-ML/translations/README.ja.md
index 563abdea..f1212c8a 100644
--- a/1-Introduction/1-intro-to-ML/translations/README.ja.md
+++ b/1-Introduction/1-intro-to-ML/translations/README.ja.md
@@ -4,7 +4,7 @@
> 馃帴 涓娿伄鐢诲儚銈掋偗銉儍銈仚銈嬨仺銆佹姊板缈掋€丄I銆佹繁灞ゅ缈掋伄閬曘亜銇仱銇勩仸瑾槑銇椼仧鍕曠敾銇岃〃绀恒仌銈屻伨銇欍€�
-## [Pre-lecture quiz](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/1?loc=ja)
+## [Pre-lecture quiz](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/1?loc=ja)
### 銈ゃ兂銉堛儹銉€銈偡銉с兂
@@ -94,7 +94,7 @@
## 馃殌 Challenge
AI銆丮L銆佹繁灞ゅ缈掋€併儑銉笺偪銈点偆銈ㄣ兂銈广伄閬曘亜銇仱銇勩仸鐞嗚В銇椼仸銇勩倠銇撱仺銈掋€佺礄銈刐Excalidraw](https://excalidraw.com/)銇仼銇偑銉炽儵銈ゃ兂銈€儣銉倰浣裤仯銇︺偣銈便儍銉併仐銇︺亸銇犮仌銇勩€傘伨銇熴€併仢銈屻仦銈屻伄鎶€琛撱亴寰楁剰銇ㄣ仚銈嬪晱椤屻伄銈€偆銉囥偄銈掑姞銇堛仸銇裤仸銇忋仩銇曘亜銆�
-## [Post-lecture quiz](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/2?loc=ja)
+## [Post-lecture quiz](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/2?loc=ja)
## 鎸倞杩斻倞銇ㄨ嚜缈�
diff --git a/1-Introduction/1-intro-to-ML/translations/README.ko.md b/1-Introduction/1-intro-to-ML/translations/README.ko.md
index b62f1577..5675daa9 100644
--- a/1-Introduction/1-intro-to-ML/translations/README.ko.md
+++ b/1-Introduction/1-intro-to-ML/translations/README.ko.md
@@ -4,7 +4,7 @@
> 馃帴 毹胳嫚霟嫕, AI 攴鸽Μ瓿� 霐ル煬雼濎潣 彀澊毳� 靹る獏頃橂姅 鞓侅儊鞚� 氤措牑氅� 鞙� 鞚措歆€毳� 韥措Ν頃╇媹雼�.
-## [臧曥潣 鞝� 韤挫](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/1/)
+## [臧曥潣 鞝� 韤挫](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/1/)
### 靻岅皽
@@ -100,7 +100,7 @@
膦呾澊鞐� 攴鸽Μ瓯半倶, [Excalidraw](https://excalidraw.com/)觳橂熂 鞓澕鞚� 鞎膘潉 鞚挫毄頃橃棳 AI, ML, 霐ル煬雼�, 攴鸽Μ瓿� 雿办澊韯� 靷澊鞏胳姢鞚� 彀澊毳� 鞚错暣頃╈嫓雼�. 臧� 旮办垹霌れ澊 鞛� 頃搓舶頃� 靾� 鞛堧姅 氍胳牅鞐� 雽€頃� 鞎勳澊霐旍柎毳� 頃╈硱氤挫劯鞖�.
-## [臧曥潣 頉� 韤挫](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/2/)
+## [臧曥潣 頉� 韤挫](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/2/)
## 毽钒 & 鞛愱赴欤茧弰 頃欖姷
diff --git a/1-Introduction/1-intro-to-ML/translations/README.pt-br.md b/1-Introduction/1-intro-to-ML/translations/README.pt-br.md
index 1f0d51ef..dba38e2d 100644
--- a/1-Introduction/1-intro-to-ML/translations/README.pt-br.md
+++ b/1-Introduction/1-intro-to-ML/translations/README.pt-br.md
@@ -4,7 +4,7 @@
> 馃帴 Clique na imagem acima para assistir um v铆deo que ilustra a diferen莽a entre machine learning, AI, e deep learning.
-## [Question谩rio inicial](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/1?loc=ptbr)
+## [Question谩rio inicial](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/1?loc=ptbr)
### Introdu莽茫o
@@ -100,7 +100,7 @@ Em um futuro pr贸ximo, compreender os fundamentos do machine learning ser谩 uma
Esboce, no papel ou usando um aplicativo online como [Excalidraw](https://excalidraw.com/), sua compreens茫o das diferen莽as entre AI, ML, deep learning e data science. Adicione algumas id茅ias de problemas que cada uma dessas t茅cnicas 茅 boa para resolver.
-## [Question谩rio p贸s-aula](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/2?loc=ptbr)
+## [Question谩rio p贸s-aula](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/2?loc=ptbr)
## Revis茫o e autoestudo
diff --git a/1-Introduction/1-intro-to-ML/translations/README.ru.md b/1-Introduction/1-intro-to-ML/translations/README.ru.md
index c61b5a52..3a415298 100644
--- a/1-Introduction/1-intro-to-ML/translations/README.ru.md
+++ b/1-Introduction/1-intro-to-ML/translations/README.ru.md
@@ -8,7 +8,7 @@
> 馃帴 袧邪卸屑懈褌械 薪邪 懈蟹芯斜褉邪卸械薪懈械 胁褘褕械, 褔褌芯斜褘 锌褉芯褋屑芯褌褉械褌褜 胁懈写械芯, 胁 泻芯褌芯褉芯屑 芯斜褋褍卸写邪械褌褋褟 褉邪蟹薪懈褑邪 屑械卸写褍 屑邪褕懈薪薪褘屑 芯斜褍褔械薪懈械屑, 懈褋泻褍褋褋褌胁械薪薪褘屑 懈薪褌械谢谢械泻褌芯屑 懈 谐谢褍斜芯泻懈屑 芯斜褍褔械薪懈械屑.
-## [孝械褋褌 锌械褉械写 谢械泻褑懈械泄](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/1/)
+## [孝械褋褌 锌械褉械写 谢械泻褑懈械泄](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/1/)
---
@@ -134,7 +134,7 @@
袧邪斜褉芯褋邪泄褌械 薪邪 斜褍屑邪谐械 懈谢懈 褋 锌芯屑芯褖褜褞 芯薪谢邪泄薪-锌褉懈谢芯卸械薪懈褟, 褌邪泻芯谐芯 泻邪泻 [Excalidraw](https://excalidraw.com/), 胁邪褕械 锌芯薪懈屑邪薪懈械 褉邪蟹谢懈褔懈泄 屑械卸写褍 AI, ML, 谐谢褍斜芯泻懈屑 芯斜褍褔械薪懈械屑 懈 薪邪褍泻芯泄 芯 写邪薪薪褘褏. 袛芯斜邪胁褜褌械 薪械褋泻芯谢褜泻芯 懈写械泄 芯 锌褉芯斜谢械屑邪褏, 泻芯褌芯褉褘械 屑芯卸械褌 褉械褕懈褌褜 泻邪卸写褘泄 懈蟹 褝褌懈褏 屑械褌芯写芯胁.
-# [孝械褋褌 锌芯褋谢械 谢械泻褑懈懈](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/2/)
+# [孝械褋褌 锌芯褋谢械 谢械泻褑懈懈](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/2/)
---
# 袨斜蟹芯褉 懈 褋邪屑芯芯斜褍褔械薪懈械
diff --git a/1-Introduction/1-intro-to-ML/translations/README.tr.md b/1-Introduction/1-intro-to-ML/translations/README.tr.md
index 82dd91e2..93206b1f 100644
--- a/1-Introduction/1-intro-to-ML/translations/README.tr.md
+++ b/1-Introduction/1-intro-to-ML/translations/README.tr.md
@@ -4,7 +4,7 @@
> 馃帴 Makine 枚臒renimi, yapay zeka ve derin 枚臒renme aras谋ndaki fark谋 tart谋艧an bir video i莽in yukar谋daki resme t谋klay谋n.
-## [Ders 枚ncesi s谋nav](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/1?loc=tr)
+## [Ders 枚ncesi s谋nav](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/1?loc=tr)
### Introduction
@@ -103,7 +103,7 @@ Yak谋n gelecekte, yayg谋n olarak benimsenmesi nedeniyle makine 枚臒reniminin tem
Ka臒谋t 眉zerinde veya [Excalidraw](https://excalidraw.com/) gibi 莽evrimi莽i bir uygulama kullanarak AI, makine 枚臒renimi, derin 枚臒renme ve veri bilimi aras谋ndaki farklar谋 anlad谋臒谋n谋zdan emin olun. Bu tekniklerin her birinin 莽枚zmede iyi oldu臒u baz谋 problem fikirleri ekleyin.
-## [Ders sonras谋 test](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/2?loc=tr)
+## [Ders sonras谋 test](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/2?loc=tr)
## 陌nceleme ve Bireysel 脟al谋艧ma
diff --git a/1-Introduction/1-intro-to-ML/translations/README.zh-cn.md b/1-Introduction/1-intro-to-ML/translations/README.zh-cn.md
index 22133bd4..977ab8ff 100644
--- a/1-Introduction/1-intro-to-ML/translations/README.zh-cn.md
+++ b/1-Introduction/1-intro-to-ML/translations/README.zh-cn.md
@@ -4,7 +4,7 @@
> 馃帴 鐐瑰嚮涓婇潰鐨勫浘鐗囪鐪嬭璁烘満鍣ㄥ涔犮€佷汉宸ユ櫤鑳藉拰娣卞害瀛︿範涔嬮棿鍖哄埆鐨勮棰戙€�
-## [璇惧墠娴嬮獙](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/1/)
+## [璇惧墠娴嬮獙](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/1/)
### 浠嬬粛
@@ -96,7 +96,7 @@
鍦ㄧ焊涓婃垨浣跨敤 [Excalidraw](https://excalidraw.com/) 绛夊湪绾垮簲鐢ㄧ▼搴忕粯鍒惰崏鍥撅紝浜嗚В浣犲 AI銆丮L銆佹繁搴﹀涔犲拰鏁版嵁绉戝涔嬮棿宸紓鐨勭悊瑙ｃ€傛坊鍔犱竴浜涘叧浜庤繖浜涙妧鏈搮闀胯В鍐崇殑闂鐨勬兂娉曘€�
-## [闃呰鍚庢祴楠宂(https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/2/)
+## [闃呰鍚庢祴楠宂(https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/2/)
## 澶嶄範涓庤嚜瀛�
diff --git a/1-Introduction/1-intro-to-ML/translations/README.zh-tw.md b/1-Introduction/1-intro-to-ML/translations/README.zh-tw.md
index 9e55679b..0902dfce 100644
--- a/1-Introduction/1-intro-to-ML/translations/README.zh-tw.md
+++ b/1-Introduction/1-intro-to-ML/translations/README.zh-tw.md
@@ -3,7 +3,7 @@
[](https://youtu.be/lTd9RSxS9ZE "姗熷櫒瀛哥繏锛屼汉宸ユ櫤鑳斤紝娣卞害瀛哥繏-鏈変粈楹藉崁鍒�?")
> 馃帴 榛炴搳涓婇潰鐨勫湒鐗囪鐪嬭◣璜栨鍣ㄥ缈掋€佷汉宸ユ櫤鑳藉拰娣卞害瀛哥繏涔嬮枔鍗€鍒ョ殑瑕栭牷銆�
-## [瑾插墠娓](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/1/)
+## [瑾插墠娓](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/1/)
### 浠嬬垂
@@ -92,7 +92,7 @@
鍦ㄧ礄涓婃垨浣跨敤 [Excalidraw](https://excalidraw.com/) 绛夊湪绶氭噳鐢ㄧ▼搴忕躬瑁借崏鍦栵紝浜嗚В浣犲皪 AI銆丮L銆佹繁搴﹀缈掑拰鏁告摎绉戝涔嬮枔宸暟鐨勭悊瑙ｃ€傛坊鍔犱竴浜涢棞鏂奸€欎簺鎶€琛撴搮闀疯В姹虹殑鍟忛鐨勬兂娉曘€�
-## [闁辫畝寰屾脯椹梋(https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/2/)
+## [闁辫畝寰屾脯椹梋(https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/2/)
## 寰╃繏鑸囪嚜瀛�
diff --git a/1-Introduction/2-history-of-ML/README.md b/1-Introduction/2-history-of-ML/README.md
index a26fce77..2bf6127f 100644
--- a/1-Introduction/2-history-of-ML/README.md
+++ b/1-Introduction/2-history-of-ML/README.md
@@ -3,7 +3,7 @@
> Sketchnote by [Tomomi Imura](https://www.twitter.com/girlie_mac)
-## [Pre-lecture quiz](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/3/)
+## [Pre-lecture quiz](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/3/)
---
@@ -128,7 +128,7 @@ It remains to be seen what the future holds, but it is important to understand t
Dig into one of these historical moments and learn more about the people behind them. There are fascinating characters, and no scientific discovery was ever created in a cultural vacuum. What do you discover?
-## [Post-lecture quiz](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/4/)
+## [Post-lecture quiz](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/4/)
---
## Review & Self Study
diff --git a/1-Introduction/2-history-of-ML/translations/README.es.md b/1-Introduction/2-history-of-ML/translations/README.es.md
index b878e4e1..116cc7e4 100755
--- a/1-Introduction/2-history-of-ML/translations/README.es.md
+++ b/1-Introduction/2-history-of-ML/translations/README.es.md
@@ -3,7 +3,7 @@
> Boceto por [Tomomi Imura](https://www.twitter.com/girlie_mac)
-## [Cuestionario previo a la conferencia](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/3?loc=es)
+## [Cuestionario previo a la conferencia](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/3?loc=es)
En esta lecci贸n, analizaremos los principales hitos en la historia del machine learning y la inteligencia artificial.
@@ -102,7 +102,7 @@ Queda por ver qu茅 depara el futuro, pero es importante entender estos sistemas
Sum茅rjase dentro de unos de estos momentos hist贸ricos y aprenda m谩s sobre las personas detr谩s de ellos. Hay personajes fascinantes y nunca ocurri贸 ning煤n descubrimiento cient铆fico en un vac铆o cultural. 驴Qu茅 descubres?
-## [Cuestionario posterior a la lecci贸n](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/4?loc=es)
+## [Cuestionario posterior a la lecci贸n](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/4?loc=es)
## Revisi贸n y autoestudio
diff --git a/1-Introduction/2-history-of-ML/translations/README.fr.md b/1-Introduction/2-history-of-ML/translations/README.fr.md
index 9c6c6687..d9ecad4f 100644
--- a/1-Introduction/2-history-of-ML/translations/README.fr.md
+++ b/1-Introduction/2-history-of-ML/translations/README.fr.md
@@ -3,7 +3,7 @@
> Sketchnote de [Tomomi Imura](https://www.twitter.com/girlie_mac)
-## [Quizz pr茅alable](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/3?loc=fr)
+## [Quizz pr茅alable](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/3?loc=fr)
Dans cette le莽on, nous allons parcourir les principales 茅tapes de l'histoire du machine learning et de l'intelligence artificielle.
@@ -102,7 +102,7 @@ Reste 脿 savoir ce que l'avenir nous r茅serve, mais il est important de comprend
Plongez dans l'un de ces moments historiques et apprenez-en plus sur les personnes derri猫re ceux-ci. Il y a des personnalit茅s fascinantes, et aucune d茅couverte scientifique n'a jamais 茅t茅 cr茅茅e avec un vide culturel. Que d茅couvrez-vous ?
-## [Quiz de validation des connaissances](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/4?loc=fr)
+## [Quiz de validation des connaissances](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/4?loc=fr)
## R茅vision et auto-apprentissage
diff --git a/1-Introduction/2-history-of-ML/translations/README.id.md b/1-Introduction/2-history-of-ML/translations/README.id.md
index 47ce2816..351dd17d 100644
--- a/1-Introduction/2-history-of-ML/translations/README.id.md
+++ b/1-Introduction/2-history-of-ML/translations/README.id.md
@@ -3,7 +3,7 @@
> Catatan sketsa oleh [Tomomi Imura](https://www.twitter.com/girlie_mac)
-## [Quiz Pra-Pelajaran](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/3/)
+## [Quiz Pra-Pelajaran](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/3/)
Dalam pelajaran ini, kita akan membahas tonggak utama dalam sejarah Machine Learning dan Artificial Intelligence.
@@ -101,7 +101,7 @@ Kita masih belum tahu apa yang akan terjadi di masa depan, tetapi penting untuk
Gali salah satu momen bersejarah ini dan pelajari lebih lanjut tentang orang-orang di baliknya. Ada karakter yang menarik, dan tidak ada penemuan ilmiah yang pernah dibuat dalam kekosongan budaya. Apa yang kamu temukan?
-## [Quiz Pasca-Pelajaran](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/4/)
+## [Quiz Pasca-Pelajaran](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/4/)
## Ulasan & Belajar Mandiri
diff --git a/1-Introduction/2-history-of-ML/translations/README.it.md b/1-Introduction/2-history-of-ML/translations/README.it.md
index f4f678aa..f95b542c 100644
--- a/1-Introduction/2-history-of-ML/translations/README.it.md
+++ b/1-Introduction/2-history-of-ML/translations/README.it.md
@@ -3,7 +3,7 @@
> Sketchnote di [Tomomi Imura](https://www.twitter.com/girlie_mac)
-## [Quiz pre-lezione](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/3/?loc=it)
+## [Quiz pre-lezione](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/3/?loc=it)
In questa lezione, si camminer脿 attraverso le principali pietre miliari nella storia di machine learning e dell'intelligenza artificiale.
@@ -103,7 +103,7 @@ Resta da vedere cosa riserva il futuro, ma 猫 importante capire questi sistemi i
Approfondire uno di questi momenti storici e scoprire
di pi霉 sulle persone che stanno dietro ad essi. Ci sono personaggi affascinanti e nessuna scoperta scientifica 猫 mai stata creata in un vuoto culturale. Cosa si 猫 scoperto?
-## [Quiz post-lezione](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/4/?loc=it)
+## [Quiz post-lezione](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/4/?loc=it)
## Revisione e Auto Apprendimento
diff --git a/1-Introduction/2-history-of-ML/translations/README.ja.md b/1-Introduction/2-history-of-ML/translations/README.ja.md
index c3eeedb2..780b1f85 100644
--- a/1-Introduction/2-history-of-ML/translations/README.ja.md
+++ b/1-Introduction/2-history-of-ML/translations/README.ja.md
@@ -3,7 +3,7 @@
> [Tomomi Imura](https://www.twitter.com/girlie_mac)銇倛銈嬨偣銈便儍銉�
-## [Pre-lecture quiz](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/3?loc=ja)
+## [Pre-lecture quiz](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/3?loc=ja)
銇撱伄鎺堟キ銇с伅銆佹姊板缈掋仺浜哄伐鐭ヨ兘銇鍙层伀銇娿亼銈嬩富瑕併仾鍑烘潵浜嬨倰绱逛粙銇椼伨銇欍€�
@@ -99,7 +99,7 @@
銇撱倢銈夈伄姝村彶鐨勭灛闁撱伄1銇ゃ倰鎺樸倞涓嬨亽銇︺€併仢銇儗寰屻伀銇勩倠浜恒€呫伀銇ゃ亜銇﹀銇炽伨銇椼倗銇嗐€傞瓍鍔涚殑銇汉銆呫亴銇勩伨銇欍仐銆佹枃鍖栫殑銇┖鐧姐伄鐘舵厠銇х瀛︾殑鐧鸿銇屻仾銇曘倢銇熴亾銇ㄣ伅銇傘倞銇俱仜銈撱€傘仼銇嗐亜銇ｃ仧銇撱仺銇岃銇ゃ亱銈嬨仹銇椼倗銇嗐亱锛�
-## [Post-lecture quiz](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/4?loc=ja)
+## [Post-lecture quiz](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/4?loc=ja)
## 鎸倞杩斻倞銇ㄨ嚜缈�
diff --git a/1-Introduction/2-history-of-ML/translations/README.ko.md b/1-Introduction/2-history-of-ML/translations/README.ko.md
index 3c61b5e7..cf0a1dae 100644
--- a/1-Introduction/2-history-of-ML/translations/README.ko.md
+++ b/1-Introduction/2-history-of-ML/translations/README.ko.md
@@ -3,7 +3,7 @@
> Sketchnote by [Tomomi Imura](https://www.twitter.com/girlie_mac)
-## [臧曥潣 鞝� 韤挫](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/3/)
+## [臧曥潣 鞝� 韤挫](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/3/)
鞚� 臧曥潣鞐愳劀, 毹胳嫚霟嫕瓿� 鞚戈车 歆€電レ潣 鞐偓鞐愳劀 欤检殧 毵堨澕鞀ろ啢鞚� 靷错幋氤措牑 頃╇媹雼�.
@@ -103,7 +103,7 @@ natural language processing 鞐瓣惮臧€ 氚滌爠頃橁碃, 瓴€靸夓澊 臧滌劆霅橃柎 雿�
鞐偓鞝侅澑 靾滉皠鞐� 靷瀸霌� 霋れ棎靹� 頃� 臧€歆€毳� 歆戩鞝侅溂搿� 韺岅碃 鞛堧姅 鞛愲ゼ 鞛愳劯頌� 鞎岇晞氤挫劯鞖�. 毵る牓鞛堧姅 旌愲Ν韯瓣皜 鞛堨溂氅�, 氍疙檾臧€ 靷澕歆� 瓿踌棎靹滊姅 瓿柬暀鞝侅澑 氚滉铂鞚� 頃橃 氇豁暕雼堧嫟. 雼轨嫚鞚€ 鞏措枻 氚滉铂鞚� 頃措炒鞎橂倶鞖�?
-## [臧曥潣 頉� 韤挫](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/4/)
+## [臧曥潣 頉� 韤挫](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/4/)
## 瓴€韱� & 鞛愱赴欤茧弰 頃欖姷
diff --git a/1-Introduction/2-history-of-ML/translations/README.pt-br.md b/1-Introduction/2-history-of-ML/translations/README.pt-br.md
index d9fa1471..815c8722 100644
--- a/1-Introduction/2-history-of-ML/translations/README.pt-br.md
+++ b/1-Introduction/2-history-of-ML/translations/README.pt-br.md
@@ -3,7 +3,7 @@
> Sketchnote por [Tomomi Imura](https://www.twitter.com/girlie_mac)
-## [Teste pr茅-aula](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/3?loc=ptbr)
+## [Teste pr茅-aula](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/3?loc=ptbr)
Nesta li莽茫o, veremos os principais marcos da hist贸ria do machine learning e da artificial intelligence.
@@ -103,7 +103,7 @@ Resta saber o que o futuro reserva, mas 茅 importante entender esses sistemas de
Explore um desses momentos hist贸ricos e aprenda mais sobre as pessoas por tr谩s deles. Existem personagens fascinantes e nenhuma descoberta cient铆fica foi criada em um v谩cuo cultural. O que voc锚 descobriu?
-## [Question谩rio p贸s-aula](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/4?loc=ptbr)
+## [Question谩rio p贸s-aula](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/4?loc=ptbr)
## Revis茫o e Autoestudo
diff --git a/1-Introduction/2-history-of-ML/translations/README.ru.md b/1-Introduction/2-history-of-ML/translations/README.ru.md
index 5dbfa2cb..013cf9cb 100644
--- a/1-Introduction/2-history-of-ML/translations/README.ru.md
+++ b/1-Introduction/2-history-of-ML/translations/README.ru.md
@@ -3,7 +3,7 @@
> 袟邪屑械褌泻邪 [孝芯屑芯屑懈 袠屑褍褉邪](https://www.twitter.com/girlie_mac)
-## [孝械褋褌 锌械褉械写 谢械泻褑懈械泄](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/3/)
+## [孝械褋褌 锌械褉械写 谢械泻褑懈械泄](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/3/)
---
@@ -128,7 +128,7 @@
袩芯谐褉褍蟹懈褌械褋褜 胁 芯写懈薪 懈蟹 褝褌懈褏 懈褋褌芯褉懈褔械褋泻懈褏 屑芯屑械薪褌芯胁 懈 褍蟹薪邪泄褌械 斜芯谢褜褕械 芯 谢褞写褟褏, 褋褌芯褟褖懈褏 蟹邪 薪懈屑懈. 袝褋褌褜 褍胁谢械泻邪褌械谢褜薪褘械 锌械褉褋芯薪邪卸懈, 懈 薪懈 芯写薪芯 薪邪褍褔薪芯械 芯褌泻褉褘褌懈械 薪懈泻芯谐写邪 薪械 褋芯蟹写邪胁邪谢芯褋褜 胁 泻褍谢褜褌褍褉薪芯屑 胁邪泻褍褍屑械. 效褌芯 胁褘 芯斜薪邪褉褍卸懈褌械?
-## [孝械褋褌 锌芯褋谢械 谢械泻褑懈懈](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/4/)
+## [孝械褋褌 锌芯褋谢械 谢械泻褑懈懈](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/4/)
---
## 袨斜蟹芯褉 懈 褋邪屑芯芯斜褍褔械薪懈械
diff --git a/1-Introduction/2-history-of-ML/translations/README.tr.md b/1-Introduction/2-history-of-ML/translations/README.tr.md
index d9277d40..4145f666 100644
--- a/1-Introduction/2-history-of-ML/translations/README.tr.md
+++ b/1-Introduction/2-history-of-ML/translations/README.tr.md
@@ -3,7 +3,7 @@
> [Tomomi Imura](https://www.twitter.com/girlie_mac) taraf谋ndan haz谋rlanan taslak-not
-## [Ders 枚ncesi test](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/3?loc=tr)
+## [Ders 枚ncesi test](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/3?loc=tr)
Bu derste, makine 枚臒renimi ve yapay zeka tarihindeki 枚nemli kilometre ta艧lar谋n谋 inceleyece臒iz.
@@ -102,7 +102,7 @@ Gelece臒in neler getirece臒ini birlikte g枚rece臒iz, ancak bu bilgisayar sisteml
Bu tarihi anlardan birine girin ve arkas谋ndaki insanlar hakk谋nda daha fazla bilgi edinin. B眉y眉leyici karakterler var ve k眉lt眉rel bir bo艧lukta hi莽bir bilimsel ke艧if yarat谋lmad谋. Ne ke艧federsiniz?
-## [Ders sonras谋 test](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/4?loc=tr)
+## [Ders sonras谋 test](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/4?loc=tr)
## 陌nceleme ve Bireysel 脟al谋艧ma
diff --git a/1-Introduction/2-history-of-ML/translations/README.zh-cn.md b/1-Introduction/2-history-of-ML/translations/README.zh-cn.md
index e1184b6d..ddd2430d 100644
--- a/1-Introduction/2-history-of-ML/translations/README.zh-cn.md
+++ b/1-Introduction/2-history-of-ML/translations/README.zh-cn.md
@@ -3,7 +3,7 @@
> 浣滆€� [Tomomi Imura](https://www.twitter.com/girlie_mac)
-## [璇惧墠娴嬮獙](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/3/)
+## [璇惧墠娴嬮獙](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/3/)
鍦ㄦ湰璇句腑锛屾垜浠皢璧拌繃鏈哄櫒瀛︿範鍜屼汉宸ユ櫤鑳藉巻鍙蹭笂鐨勪富瑕侀噷绋嬬銆�
@@ -101,7 +101,7 @@ Alan Turing锛屼竴涓湡姝ｆ澃鍑虹殑浜猴紝[鍦� 2019 骞磋鍏紬鎶曠エ閫夊嚭](htt
娣卞叆浜嗚В杩欎簺鍘嗗彶鏃跺埢涔嬩竴锛屽苟鏇村鍦颁簡瑙ｅ畠浠儗鍚庣殑浜恒€傝繖閲屾湁璁稿寮曚汉鍏ヨ儨鐨勪汉鐗╋紝娌℃湁涓€椤圭瀛﹀彂鐜版槸鍦ㄦ枃鍖栫湡绌轰腑鍒涢€犲嚭鏉ョ殑銆備綘鍙戠幇浜嗕粈涔堬紵
-## [璇惧悗娴嬮獙](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/4/)
+## [璇惧悗娴嬮獙](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/4/)
## 澶嶄範涓庤嚜瀛�
diff --git a/1-Introduction/2-history-of-ML/translations/README.zh-tw.md b/1-Introduction/2-history-of-ML/translations/README.zh-tw.md
index 4fb491d2..7c88c2ee 100644
--- a/1-Introduction/2-history-of-ML/translations/README.zh-tw.md
+++ b/1-Introduction/2-history-of-ML/translations/README.zh-tw.md
@@ -2,7 +2,7 @@
> 浣滆€� [Tomomi Imura](https://www.twitter.com/girlie_mac)
-## [瑾插墠娓](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/3/)
+## [瑾插墠娓](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/3/)
鍦ㄦ湰瑾蹭腑锛屾垜鍊戝皣璧伴亷姗熷櫒瀛哥繏鍜屼汉宸ユ櫤鑳芥鍙蹭笂鐨勪富瑕佽绋嬬銆�
@@ -95,7 +95,7 @@ Alan Turing锛屼竴鍊嬬湡姝ｅ倯鍑虹殑浜猴紝[鍦� 2019 骞磋鍏溇鎶曠エ閬稿嚭](htt
娣卞叆浜嗚В閫欎簺姝峰彶鏅傚埢涔嬩竴锛屼甫鏇村鍦颁簡瑙ｅ畠鍊戣儗寰岀殑浜恒€傞€欒鏈夎ū澶氬紩浜哄叆鍕濈殑浜虹墿锛屾矑鏈変竴闋呯瀛哥櫦鐝炬槸鍦ㄦ枃鍖栫湡绌轰腑鍓甸€犲嚭渚嗙殑銆備綘鐧肩従浜嗕粈楹斤紵
-## [瑾插緦娓](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/4/)
+## [瑾插緦娓](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/4/)
## 寰╃繏鑸囪嚜瀛�
diff --git a/1-Introduction/3-fairness/README.md b/1-Introduction/3-fairness/README.md
index baa27562..9e846cb7 100644
--- a/1-Introduction/3-fairness/README.md
+++ b/1-Introduction/3-fairness/README.md
@@ -3,7 +3,7 @@
> Sketchnote by [Tomomi Imura](https://www.twitter.com/girlie_mac)
-## [Pre-lecture quiz](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/5/)
+## [Pre-lecture quiz](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/5/)
## Introduction
@@ -184,7 +184,7 @@ To prevent biases from being introduced in the first place, we should:
Think about real-life scenarios where unfairness is evident in model-building and usage. What else should we consider?
-## [Post-lecture quiz](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/6/)
+## [Post-lecture quiz](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/6/)
## Review & Self Study
In this lesson, you have learned some basics of the concepts of fairness and unfairness in machine learning.
diff --git a/1-Introduction/3-fairness/translations/README.es.md b/1-Introduction/3-fairness/translations/README.es.md
index d6c71df9..39afddd0 100644
--- a/1-Introduction/3-fairness/translations/README.es.md
+++ b/1-Introduction/3-fairness/translations/README.es.md
@@ -3,7 +3,7 @@
> Sketchnote por [Tomomi Imura](https://www.twitter.com/girlie_mac)
-## [Examen previo a la lecci贸n](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/5?loc=es)
+## [Examen previo a la lecci贸n](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/5?loc=es)
## Introducci贸n
@@ -183,7 +183,7 @@ Para prevenir que los sesgos sean introducidos en primer lugar, debemos:
Piensa en escenarios de la vida real donde la injusticia es evidente en la construcci贸n y uso de modelos. 驴Qu茅 m谩s debemos considerar?
-## [Cuestionario posterior a la lecci贸n](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/6?loc=es)
+## [Cuestionario posterior a la lecci贸n](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/6?loc=es)
## Revisi贸n y autoestudio
En esta lecci贸n has aprendido algunos de los conceptos b谩sicos de justicia e injusticia en el aprendizaje autom谩tico.
diff --git a/1-Introduction/3-fairness/translations/README.fr.md b/1-Introduction/3-fairness/translations/README.fr.md
index 73e2ef52..3313b6a0 100644
--- a/1-Introduction/3-fairness/translations/README.fr.md
+++ b/1-Introduction/3-fairness/translations/README.fr.md
@@ -3,7 +3,7 @@
> Sketchnote par [Tomomi Imura](https://www.twitter.com/girlie_mac)
-## [Quiz pr茅alable](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/5/?loc=fr)
+## [Quiz pr茅alable](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/5/?loc=fr)
## Introduction
@@ -184,7 +184,7 @@ Pour 茅viter que des biais ne soient introduits en premier lieu, nous devrions聽
Pensez 脿 des sc茅narios de la vie r茅elle o霉 l'injustice est 茅vidente dans la construction et l'utilisation de mod猫les. Que devrions-nous consid茅rer d'autre ?
-## [Quiz de validation des connaissances](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/6/?loc=fr)
+## [Quiz de validation des connaissances](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/6/?loc=fr)
## R茅vision et auto-apprentissage
Dans cette le莽on, nous avons appris quelques notions de base sur les concepts d'茅quit茅 et d'injustice dans le machine learning.
diff --git a/1-Introduction/3-fairness/translations/README.id.md b/1-Introduction/3-fairness/translations/README.id.md
index 053960d8..a01b3bc5 100644
--- a/1-Introduction/3-fairness/translations/README.id.md
+++ b/1-Introduction/3-fairness/translations/README.id.md
@@ -3,7 +3,7 @@
> Catatan sketsa oleh [Tomomi Imura](https://www.twitter.com/girlie_mac)
-## [Quiz Pra-Pelajaran](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/5/)
+## [Quiz Pra-Pelajaran](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/5/)
## Pengantar
@@ -185,7 +185,7 @@ Untuk mencegah kemunculan bias pada awalnya, kita harus:
Pikirkan tentang skenario kehidupan nyata di mana ketidakadilan terbukti dalam pembuatan dan penggunaan model. Apa lagi yang harus kita pertimbangkan?
-## [Quiz Pasca-Pelajaran](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/6/)
+## [Quiz Pasca-Pelajaran](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/6/)
## Ulasan & Belajar Mandiri
Dalam pelajaran ini, Kamu telah mempelajari beberapa dasar konsep keadilan dan ketidakadilan dalam pembelajaran mesin.
diff --git a/1-Introduction/3-fairness/translations/README.it.md b/1-Introduction/3-fairness/translations/README.it.md
index a9440c90..7176608e 100644
--- a/1-Introduction/3-fairness/translations/README.it.md
+++ b/1-Introduction/3-fairness/translations/README.it.md
@@ -3,7 +3,7 @@
> Sketchnote di [Tomomi Imura](https://www.twitter.com/girlie_mac)
-## [Quiz pre-lezione](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/5/?loc=it)
+## [Quiz pre-lezione](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/5/?loc=it)
## Introduzione
@@ -183,7 +183,7 @@ Per evitare che vengano introdotti pregiudizi, in primo luogo, si dovrebbe:
Si pensi a scenari di vita reale in cui l'ingiustizia 猫 evidente nella creazione e nell'utilizzo del modello. Cos'altro si dovrebbe considerare?
-## [Quiz post-lezione](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/6/?loc=it)
+## [Quiz post-lezione](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/6/?loc=it)
## Revisione e Auto Apprendimento
diff --git a/1-Introduction/3-fairness/translations/README.ja.md b/1-Introduction/3-fairness/translations/README.ja.md
index e5a3d21e..44ce4c71 100644
--- a/1-Introduction/3-fairness/translations/README.ja.md
+++ b/1-Introduction/3-fairness/translations/README.ja.md
@@ -3,7 +3,7 @@
> [Tomomi Imura](https://www.twitter.com/girlie_mac)銇倛銈嬨偣銈便儍銉�
-## [Pre-lecture quiz](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/5?loc=ja)
+## [Pre-lecture quiz](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/5?loc=ja)
## 銈ゃ兂銉堛儹銉€銈偡銉с兂
@@ -178,7 +178,7 @@ AI銈勬姊板缈掋伀銇娿亼銈嬪叕骞虫€с伄淇濊ḿ銇€佷緷鐒躲仺銇椼仸瑜囬洃銇ぞ
銉€儑銉伄妲嬬瘔銈勪娇鐢ㄣ伀銇娿亜銇︺€佷笉鍏钩銇屾槑銈夈亱銇仾銈嬨倛銇嗐仾鐝惧疅銇偡銉娿儶銈倰鑰冦亪銇︺伩銇︺亸銇犮仌銇勩€備粬銇仼銇倛銇嗐仾銇撱仺銈掕€冦亪銈嬨伖銇嶃仹銇椼倗銇嗐亱锛�
-## [Post-lecture quiz](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/6?loc=ja)
+## [Post-lecture quiz](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/6?loc=ja)
## Review & Self Study
銇撱伄銉儍銈广兂銇с伅銆佹姊板缈掋伀銇娿亼銈嬪叕骞炽€佷笉鍏钩銇蹇点伄鍩虹銈掑銇炽伨銇椼仧銆�
diff --git a/1-Introduction/3-fairness/translations/README.ko.md b/1-Introduction/3-fairness/translations/README.ko.md
index 4718dcc1..0d833e19 100644
--- a/1-Introduction/3-fairness/translations/README.ko.md
+++ b/1-Introduction/3-fairness/translations/README.ko.md
@@ -3,7 +3,7 @@
> Sketchnote by [Tomomi Imura](https://www.twitter.com/girlie_mac)
-## [臧曥潣 鞝� 韤挫](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/5/)
+## [臧曥潣 鞝� 韤挫](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/5/)
## 靻岅皽
@@ -185,7 +185,7 @@ AI鞕€ 毹胳嫚霟嫕鞚� 瓿奠爼靹膘潉 氤挫灔頃橂姅 瓯� 瓿勳啀 氤奠灐頃� 靷殞旮�
氇嵏鞚� 甑稌頃橁碃 靷毄頃橂┐靹� 攵堦车鞝曧暅 鞁�-靸濏櫆 鞁滊倶毽槫毳� 靸濌皝頃措炒靹胳殧. 鞏措柣瓴� 瓿犽牑頃挫暭 頃橂倶鞖�?
-## [臧曥潣 頉� 韤挫](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/6/)
+## [臧曥潣 頉� 韤挫](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/6/)
## 瓴€韱� & 鞛愱赴欤茧弰 頃欖姷
diff --git a/1-Introduction/3-fairness/translations/README.pt-br.md b/1-Introduction/3-fairness/translations/README.pt-br.md
index 9bb5f629..0617e88a 100644
--- a/1-Introduction/3-fairness/translations/README.pt-br.md
+++ b/1-Introduction/3-fairness/translations/README.pt-br.md
@@ -3,7 +3,7 @@
> Sketchnote por [Tomomi Imura](https://www.twitter.com/girlie_mac)
-## [Teste pr茅-aula](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/5?loc=ptbr)
+## [Teste pr茅-aula](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/5?loc=ptbr)
## Introdu莽茫o
@@ -182,7 +182,7 @@ Para evitar que preconceitos sejam introduzidos em primeiro lugar, devemos:
Pense em cen谩rios da vida real onde a injusti莽a 茅 evidente na constru莽茫o e uso de modelos. O que mais devemos considerar?
-## [Question谩rio p贸s-aula](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/6?loc=ptbr)
+## [Question谩rio p贸s-aula](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/6?loc=ptbr)
## Revis茫o e Autoestudo
diff --git a/1-Introduction/3-fairness/translations/README.zh-cn.md b/1-Introduction/3-fairness/translations/README.zh-cn.md
index b8f6fa3d..99531065 100644
--- a/1-Introduction/3-fairness/translations/README.zh-cn.md
+++ b/1-Introduction/3-fairness/translations/README.zh-cn.md
@@ -3,7 +3,7 @@
-## [璇惧墠娴嬮獙](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/5/)
+## [璇惧墠娴嬮獙](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/5/)
## 浠嬬粛
@@ -186,7 +186,7 @@
鎯虫兂鐜板疄鐢熸椿涓殑鍦烘櫙锛屽湪妯″瀷鏋勫缓鍜屼娇鐢ㄤ腑鏄庢樉瀛樺湪涓嶅叕骞炽€傛垜浠繕搴旇鑰冭檻浠€涔堬紵
-## [璇惧悗娴嬮獙](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/6/)
+## [璇惧悗娴嬮獙](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/6/)
## 澶嶄範涓庤嚜瀛�
鍦ㄦ湰璇句腑锛屼綘瀛︿範浜嗘満鍣ㄥ涔犱腑鍏钩鍜屼笉鍏钩姒傚康鐨勪竴浜涘熀纭€鐭ヨ瘑銆�
diff --git a/1-Introduction/3-fairness/translations/README.zh-tw.md b/1-Introduction/3-fairness/translations/README.zh-tw.md
index db84f46d..0029402e 100644
--- a/1-Introduction/3-fairness/translations/README.zh-tw.md
+++ b/1-Introduction/3-fairness/translations/README.zh-tw.md
@@ -2,7 +2,7 @@
-## [瑾插墠娓](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/5/)
+## [瑾插墠娓](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/5/)
## 浠嬬垂
@@ -181,7 +181,7 @@
鎯虫兂鐝惧鐢熸椿涓殑鍫存櫙锛屽湪妯″瀷妲嬪缓鍜屼娇鐢ㄤ腑鏄庨’瀛樺湪涓嶅叕骞炽€傛垜鍊戦倓鎳夎┎鑰冩叜浠€楹斤紵
-## [瑾插緦娓](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/6/)
+## [瑾插緦娓](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/6/)
## 寰╃繏鑸囪嚜瀛�
鍦ㄦ湰瑾蹭腑锛屼綘瀛哥繏浜嗘鍣ㄥ缈掍腑鍏钩鍜屼笉鍏钩姒傚康鐨勪竴浜涘熀绀庣煡璀樸€�
diff --git a/1-Introduction/4-techniques-of-ML/README.md b/1-Introduction/4-techniques-of-ML/README.md
index 01e40fac..171ac6ac 100644
--- a/1-Introduction/4-techniques-of-ML/README.md
+++ b/1-Introduction/4-techniques-of-ML/README.md
@@ -5,7 +5,7 @@ The process of building, using, and maintaining machine learning models and the
- Understand the processes underpinning machine learning at a high level.
- Explore base concepts such as 'models', 'predictions', and 'training data'.
-## [Pre-lecture quiz](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/7/)
+## [Pre-lecture quiz](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/7/)
## Introduction
@@ -103,7 +103,7 @@ In these lessons, you will discover how to use these steps to prepare, build, te
Draw a flow chart reflecting the steps of a ML practitioner. Where do you see yourself right now in the process? Where do you predict you will find difficulty? What seems easy to you?
-## [Post-lecture quiz](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/8/)
+## [Post-lecture quiz](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/8/)
## Review & Self Study
diff --git a/1-Introduction/4-techniques-of-ML/translations/README.es.md b/1-Introduction/4-techniques-of-ML/translations/README.es.md
index e5458c13..ffcda229 100755
--- a/1-Introduction/4-techniques-of-ML/translations/README.es.md
+++ b/1-Introduction/4-techniques-of-ML/translations/README.es.md
@@ -6,7 +6,7 @@ El proceso de creaci贸n, uso y mantenimiento de modelos de machine learning, y l
- Explorar conceptos b谩sicos como 'modelos', 'predicciones', y 'datos de entrenamiento'
-## [Cuestionario previo a la conferencia](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/7?loc=es)
+## [Cuestionario previo a la conferencia](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/7?loc=es)
## Introducci贸n
A un alto nivel, el arte de crear procesos de machine learning (ML) se compone de una serie de pasos:
@@ -101,7 +101,7 @@ En estas lecciones, descubrir谩 c贸mo utilizar estos pasos para preparar, constr
Dibuje un diagrama de flujos que refleje los pasos de practicante de ML. 驴D贸nde te ves ahora mismo en el proceso? 驴D贸nde predice que encontrar谩 dificultades? 驴Qu茅 te parece f谩cil?
-## [Cuestionario posterior a la conferencia](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/8?loc=es)
+## [Cuestionario posterior a la conferencia](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/8?loc=es)
## Revisi贸n & Autoestudio
diff --git a/1-Introduction/4-techniques-of-ML/translations/README.id.md b/1-Introduction/4-techniques-of-ML/translations/README.id.md
index 47e7c5b8..e9a27e25 100644
--- a/1-Introduction/4-techniques-of-ML/translations/README.id.md
+++ b/1-Introduction/4-techniques-of-ML/translations/README.id.md
@@ -5,7 +5,7 @@ Proses membangun, menggunakan, dan memelihara model machine learning dan data ya
- Memahami gambaran dari proses yang mendasari machine learning.
- Menjelajahi konsep dasar seperti '*models*', '*predictions*', dan '*training data*'.
-## [Quiz Pra-Pelajaran](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/7/)
+## [Quiz Pra-Pelajaran](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/7/)
## Pengantar
Gambaran membuat proses machine learning (ML) terdiri dari sejumlah langkah:
@@ -100,7 +100,7 @@ Dalam pelajaran ini, Kamu akan menemukan cara untuk menggunakan langkah-langkah
Gambarlah sebuah flow chart yang mencerminkan langkah-langkah seorang praktisi ML. Di mana kamu melihat diri kamu saat ini dalam prosesnya? Di mana kamu memprediksi kamu akan menemukan kesulitan? Apa yang tampak mudah bagi kamu?
-## [Quiz Pra-Pelajaran](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/8/)
+## [Quiz Pra-Pelajaran](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/8/)
## Ulasan & Belajar Mandiri
diff --git a/1-Introduction/4-techniques-of-ML/translations/README.it.md b/1-Introduction/4-techniques-of-ML/translations/README.it.md
index b6602f98..5222efaa 100644
--- a/1-Introduction/4-techniques-of-ML/translations/README.it.md
+++ b/1-Introduction/4-techniques-of-ML/translations/README.it.md
@@ -5,7 +5,7 @@ Il processo di creazione, utilizzo e mantenimento dei modelli di machine learnin
- Comprendere i processi ad alto livello alla base di machine learning.
- Esplorare concetti di base come "modelli", "previsioni" e "dati di addestramento".
-## [Quiz pre-lezione](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/7/?loc=it)
+## [Quiz pre-lezione](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/7/?loc=it)
## Introduzione
@@ -103,7 +103,7 @@ In queste lezioni si scoprir脿 come utilizzare questi passaggi per preparare, co
Disegnare un diagramma di flusso che rifletta i passaggi di un professionista di ML. Dove ci si vede in questo momento nel processo? Dove si prevede che sorgeranno difficolt脿? Cosa sembra facile?
-## [Quiz post-lezione](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/8/?loc=it)
+## [Quiz post-lezione](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/8/?loc=it)
## Revisione e Auto Apprendimento
diff --git a/1-Introduction/4-techniques-of-ML/translations/README.ja.md b/1-Introduction/4-techniques-of-ML/translations/README.ja.md
index e6880689..5cb00144 100644
--- a/1-Introduction/4-techniques-of-ML/translations/README.ja.md
+++ b/1-Introduction/4-techniques-of-ML/translations/README.ja.md
@@ -5,7 +5,7 @@
- 姗熸瀛︾繏銈掓敮銇堛倠銉椼儹銈汇偣銈掗珮銇勬按婧栥仹鐞嗚В銇椼伨銇欍€�
- 銆屻儮銉囥儷銆嶃€屼簣娓€嶃€岃〒绶淬儑銉笺偪銆嶃仾銇┿伄鍩烘湰鐨勩仾姒傚康銈掕銇广伨銇欍€�
-## [璎涚京鍓嶃伄灏忋儐銈广儓](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/7?loc=ja)
+## [璎涚京鍓嶃伄灏忋儐銈广儓](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/7?loc=ja)
## 灏庡叆
@@ -103,7 +103,7 @@
姗熸瀛︾繏銇缈掕€呫伄銈广儐銉冦儣銈掑弽鏄犮仐銇熴儠銉兗銉併儯銉笺儓銈掓弿銇勩仸銇忋仩銇曘亜銆備粖銇嚜鍒嗐伅銇撱伄銉椼儹銈汇偣銇仼銇撱伀銇勩倠銇ㄦ€濄亜銇俱仚銇嬶紵銇┿亾銇洶闆ｃ亴銇傘倠銇ㄤ簣鎯炽仐銇俱仚銇嬶紵銇傘仾銇熴伀銇ㄣ仯銇︾啊鍗樸仢銇嗐仾銇撱仺銇綍銇с仚銇嬶紵
-## [璎涚京寰屻伄灏忋儐銈广儓](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/8?loc=ja)
+## [璎涚京寰屻伄灏忋儐銈广儓](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/8?loc=ja)
## 鎸倞杩斻倞銇ㄨ嚜涓诲缈�
diff --git a/1-Introduction/4-techniques-of-ML/translations/README.ko.md b/1-Introduction/4-techniques-of-ML/translations/README.ko.md
index 7126a3a4..a57fc027 100644
--- a/1-Introduction/4-techniques-of-ML/translations/README.ko.md
+++ b/1-Introduction/4-techniques-of-ML/translations/README.ko.md
@@ -5,7 +5,7 @@
- 毹胳嫚霟嫕鞚� 氚涭硱欤茧姅 頂勲靹胳姢毳� 瓿犾垬欷€鞐愳劀 鞚错暣頃╇媹雼�.
- 'models', 'predictions', 攴鸽Μ瓿� 'training data'鞕€ 臧欖潃 旮办磮 臧滊厫鞚� 韮愳儔頃╇媹雼�.
-## [臧曥潣 鞝� 韤挫](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/7/)
+## [臧曥潣 鞝� 韤挫](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/7/)
## 靻岅皽
@@ -103,7 +103,7 @@ feature電� 雿办澊韯办潣 旄§爼頃� 靾� 鞛堧姅 靻嶌劚鞛呺媹雼�. 毵庫潃 雿办澊韯�
ML 鞁る鞛愳潣 雼硠毳� 氚橃榿頃� 頂岆鞖半ゼ 攴鸽牑氤挫劯鞖�. 頂勲靹胳姢鞐愳劀 歆€旮� 鞏措敂鞐� 鞛堧姅 歆€ 氤挫澊雮橃殧? 鞏措牑鞖� 雮挫毄鞚� 鞓堨儊頃� 靾� 鞛堧倶鞖�? 鞏措枻瓴� 靿毟旯岇殧?
-## [臧曥潣 頉� 韤挫](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/8/)
+## [臧曥潣 頉� 韤挫](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/8/)
## 瓴€韱� & 鞛愱赴欤茧弰 頃欖姷
diff --git a/1-Introduction/4-techniques-of-ML/translations/README.pt-br.md b/1-Introduction/4-techniques-of-ML/translations/README.pt-br.md
index 935dbe62..683c3a31 100644
--- a/1-Introduction/4-techniques-of-ML/translations/README.pt-br.md
+++ b/1-Introduction/4-techniques-of-ML/translations/README.pt-br.md
@@ -5,7 +5,7 @@ O processo de constru莽茫o, uso e manuten莽茫o de modelos de machine learning e
- Compreender os processos que sustentam o aprendizado de m谩quina em alto n铆vel.
- Explorar conceitos b谩sicos como 'modelos', 'previs玫es' e 'dados de treinamento'..
-## [Question谩rio pr茅-aula](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/7?loc=ptbr)
+## [Question谩rio pr茅-aula](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/7?loc=ptbr)
## Introdu莽茫o
@@ -103,7 +103,7 @@ Nessas li莽玫es, voc锚 descobrir谩 como usar essas etapas para preparar, criar,
Desenhe um fluxograma refletindo as etapas de um praticante de ML. Onde voc锚 se v锚 agora no processo? Onde voc锚 prev锚 que encontrar谩 dificuldade? O que parece f谩cil para voc锚?
-## [Question谩rio p贸s-aula](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/8?loc=ptbr)
+## [Question谩rio p贸s-aula](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/8?loc=ptbr)
## Revis茫o e Autoestudo
diff --git a/1-Introduction/4-techniques-of-ML/translations/README.zh-cn.md b/1-Introduction/4-techniques-of-ML/translations/README.zh-cn.md
index 40f1e669..bc5000c1 100644
--- a/1-Introduction/4-techniques-of-ML/translations/README.zh-cn.md
+++ b/1-Introduction/4-techniques-of-ML/translations/README.zh-cn.md
@@ -6,7 +6,7 @@
- 鍦ㄩ珮灞傛涓婄悊瑙ｆ敮鎸佹満鍣ㄥ涔犵殑杩囩▼銆�
- 鎺㈢储鍩烘湰姒傚康锛屼緥濡傗€滄ā鍨嬧€濄€佲€滈娴嬧€濆拰鈥滆缁冩暟鎹€濄€�
-## [璇惧墠娴嬮獙](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/7/)
+## [璇惧墠娴嬮獙](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/7/)
## 浠嬬粛
鍦ㄨ緝楂樼殑灞傛涓婏紝鍒涘缓鏈哄櫒瀛︿範锛圡L锛夎繃绋嬬殑宸ヨ壓鍖呮嫭璁稿姝ラ锛�
@@ -101,7 +101,7 @@
鐢讳竴涓祦绋嬪浘锛屽弽鏄燤L鐨勬楠ゃ€傚湪杩欎釜杩囩▼涓紝浣犺涓鸿嚜宸辩幇鍦ㄥ湪鍝噷锛熶綘棰勬祴浣犲湪鍝噷浼氶亣鍒板洶闅撅紵浠€涔堝浣犳潵璇村緢瀹规槗锛�
-## [闃呰鍚庢祴楠宂(https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/8/)
+## [闃呰鍚庢祴楠宂(https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/8/)
## 澶嶄範涓庤嚜瀛�
diff --git a/1-Introduction/4-techniques-of-ML/translations/README.zh-tw.md b/1-Introduction/4-techniques-of-ML/translations/README.zh-tw.md
index 8d1222be..1f725d25 100644
--- a/1-Introduction/4-techniques-of-ML/translations/README.zh-tw.md
+++ b/1-Introduction/4-techniques-of-ML/translations/README.zh-tw.md
@@ -6,7 +6,7 @@
- 鍦ㄩ珮灞ゆ涓婄悊瑙ｆ敮鎸佹鍣ㄥ缈掔殑閬庣▼銆�
- 鎺㈢储鍩烘湰姒傚康锛屼緥濡傘€屾ā鍨嬨€嶃€併€岄爯娓€嶅拰銆岃〒绶存暩鎿氥€嶃€�
-## [瑾插墠娓](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/7/)
+## [瑾插墠娓](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/7/)
## 浠嬬垂
鍦ㄨ純楂樼殑灞ゆ涓婏紝鍓靛缓姗熷櫒瀛哥繏锛圡L锛夐亷绋嬬殑宸ヨ棟鍖呮嫭瑷卞姝ラ锛�
@@ -100,7 +100,7 @@
鐣竴鍊嬫祦绋嬪湒锛屽弽鏄燤L鐨勬椹熴€傚湪閫欏€嬮亷绋嬩腑锛屼綘瑾嶇偤鑷繁鐝惧湪鍦ㄥ摢瑁忥紵浣犻爯娓綘鍦ㄥ摢瑁忔渻閬囧埌鍥伴洠锛熶粈楹藉皪浣犱締瑾緢瀹规槗锛�
-## [闁辫畝寰屾脯椹梋(https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/8/)
+## [闁辫畝寰屾脯椹梋(https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/8/)
## 寰╃繏鑸囪嚜瀛�
diff --git a/2-Regression/1-Tools/README.md b/2-Regression/1-Tools/README.md
index fdd22b69..263e500c 100644
--- a/2-Regression/1-Tools/README.md
+++ b/2-Regression/1-Tools/README.md
@@ -4,7 +4,7 @@
> Sketchnote by [Tomomi Imura](https://www.twitter.com/girlie_mac)
-## [Pre-lecture quiz](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/9/)
+## [Pre-lecture quiz](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/9/)
> ### [This lesson is available in R!](./solution/R/lesson_1-R.ipynb)
@@ -199,7 +199,7 @@ Congratulations, you built your first linear regression model, created a predict
## 馃殌Challenge
Plot a different variable from this dataset. Hint: edit this line: `X = X[:, np.newaxis, 2]`. Given this dataset's target, what are you able to discover about the progression of diabetes as a disease?
-## [Post-lecture quiz](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/10/)
+## [Post-lecture quiz](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/10/)
## Review & Self Study
diff --git a/2-Regression/1-Tools/translations/README.id.md b/2-Regression/1-Tools/translations/README.id.md
index 26cb6eee..e9963f3b 100644
--- a/2-Regression/1-Tools/translations/README.id.md
+++ b/2-Regression/1-Tools/translations/README.id.md
@@ -4,7 +4,7 @@
> Catatan sketsa oleh [Tomomi Imura](https://www.twitter.com/girlie_mac)
-## [Kuis Pra-ceramah](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/9/)
+## [Kuis Pra-ceramah](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/9/)
## Pembukaan
Dalam keempat pelajaran ini, kamu akan belajar bagaimana membangun model regresi. Kita akan berdiskusi apa fungsi model tersebut dalam sejenak. Tetapi sebelum kamu melakukan apapun, pastikan bahwa kamu sudah mempunyai alat-alat yang diperlukan untuk memulai!
@@ -195,7 +195,7 @@ Selamat, kamu telah membangun model regresi linear pertamamu, membuat sebuah pre
## Tantangan
Gambarkan sebuah variabel yang beda dari *dataset* ini. Petunjuk: edit baris ini: `X = X[:, np.newaxis, 2]`. Mengetahui target *dataset* ini, apa yang kamu bisa menemukan tentang kemajuan diabetes sebagai sebuah penyakit?
-## [Kuis pasca-ceramah](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/10/)
+## [Kuis pasca-ceramah](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/10/)
## Review & Pembelajaran Mandiri
diff --git a/2-Regression/1-Tools/translations/README.it.md b/2-Regression/1-Tools/translations/README.it.md
index dba63593..97121fad 100644
--- a/2-Regression/1-Tools/translations/README.it.md
+++ b/2-Regression/1-Tools/translations/README.it.md
@@ -4,7 +4,7 @@
> Sketchnote di [Tomomi Imura](https://www.twitter.com/girlie_mac)
-## [Qui Pre-lezione](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/9/?loc=it)
+## [Qui Pre-lezione](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/9/?loc=it)
## Introduzione
@@ -197,7 +197,7 @@ Congratulazioni, si 猫 costruito il primo modello di regressione lineare, creato
Tracciare una variabile diversa da questo insieme di dati. Suggerimento: modificare questa riga: `X = X[:, np.newaxis, 2]`. Dato l'obiettivo di questo insieme di dati, cosa si potrebbe riuscire a scoprire circa la progressione del diabete come matattia?
-## [Qui post-lezione](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/10/?loc=it)
+## [Qui post-lezione](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/10/?loc=it)
## Riepilogo e Auto Apprendimento
diff --git a/2-Regression/1-Tools/translations/README.ja.md b/2-Regression/1-Tools/translations/README.ja.md
index 626f4714..dc0207ba 100644
--- a/2-Regression/1-Tools/translations/README.ja.md
+++ b/2-Regression/1-Tools/translations/README.ja.md
@@ -4,7 +4,7 @@
> [Tomomi Imura](https://www.twitter.com/girlie_mac) 銇倛銇ｃ仸鍒朵綔銇曘倢銇熴偣銈便儍銉併儙銉笺儓
-## [璎涚京鍓嶃偗銈ゃ偤](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/9?loc=ja)
+## [璎涚京鍓嶃偗銈ゃ偤](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/9?loc=ja)
## 銈ゃ兂銉堛儹銉€銈偡銉с兂
@@ -205,7 +205,7 @@ Scikit-learn銇€併儮銉囥儷銈掓绡夈仐銆佽⿻渚°倰琛屻仯銇﹀疅闅涖伀鍒╃敤銇�
## 馃殌銉併儯銉兂銈�
銇撱伄銉囥兗銈裤偦銉冦儓銇嬨倝鍒ャ伄澶夋暟銈掗伕鎶炪仐銇︺儣銉儍銉堛仐銇︺亸銇犮仌銇勩€傘儝銉炽儓锛� `X = X[:, np.newaxis, 2]` 銇銈掔法闆嗐仚銈嬨€備粖鍥炪伄銉囥兗銈裤偦銉冦儓銇偪銉笺偛銉冦儓銇с亗銈嬨€佺硸灏跨梾銇ㄣ亜銇嗙梾姘椼伄閫茶銇仱銇勩仸銆併仼銇倛銇嗐仾鐧鸿銇屻亗銈嬨伄銇с仐銈囥亞銇嬶紵
-## [璎涚京寰屻偗銈ゃ偤](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/10?loc=ja)
+## [璎涚京寰屻偗銈ゃ偤](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/10?loc=ja)
## 銉儞銉ャ兗 & 鑷富瀛︾繏
diff --git a/2-Regression/1-Tools/translations/README.ko.md b/2-Regression/1-Tools/translations/README.ko.md
index 040401b0..bc0ac3f5 100644
--- a/2-Regression/1-Tools/translations/README.ko.md
+++ b/2-Regression/1-Tools/translations/README.ko.md
@@ -4,7 +4,7 @@
> Sketchnote by [Tomomi Imura](https://www.twitter.com/girlie_mac)
-## [臧曥潣 鞝� 韤挫](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/9/)
+## [臧曥潣 鞝� 韤挫](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/9/)
## 靻岅皽
@@ -200,7 +200,7 @@ Scikit-learn 靷毄頃橂┐ 鞓皵毳搓矊 氇嵏鞚� 毵岆摛瓿� 靷毄頃橁赴 鞙勴暣
鞚� 雿办澊韯办厠鞚€ 雼るジ 氤€靾橂ゼ Plot 頃╇媹雼�. 頌岉姼: 鞚� 霛检澑鞚� 靾橃爼頃╇媹雼�: `X = X[:, np.newaxis, 2]`. 鞚� 雿办澊韯办厠鞚� 韮€瓴熿澊 欤检柎歆� 霑�, 歆堧硲鞙茧 雼闺嚚臧€ 歆勴枆霅橂┐ 鞏措枻 瓴冹潉 韮愳儔頃� 靾� 鞛堧倶鞖�?
-## [臧曥潣 頉� 韤挫](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/10/)
+## [臧曥潣 頉� 韤挫](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/10/)
## 瓴€韱� & 鞛愱赴欤茧弰 頃欖姷
diff --git a/2-Regression/1-Tools/translations/README.pt-br.md b/2-Regression/1-Tools/translations/README.pt-br.md
index fbbc7748..4cb68e5a 100644
--- a/2-Regression/1-Tools/translations/README.pt-br.md
+++ b/2-Regression/1-Tools/translations/README.pt-br.md
@@ -4,7 +4,7 @@
> _Sketchnote_ por [Tomomi Imura](https://www.twitter.com/girlie_mac)
-## [Question谩rio inicial](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/9?loc=ptbr)
+## [Question谩rio inicial](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/9?loc=ptbr)
> ### [Esta li莽茫o est谩 dispon铆vel em R!](../solution/R/lesson_1-R.ipynb)
@@ -200,7 +200,7 @@ Parab茅ns, usando um conjunto de dados, voc锚 construiu seu primeiro modelo de r
## 馃殌Desafio
Plote uma vari谩vel diferente desse mesmo conjunto de dados. Dica: edite a linha: `X = X[:, np.newaxis, 2]`. Dado o conjunto de dados alvo, o que pode ser descoberto sobre o progresso da diabetes como uma doen莽a?
-## [Question谩rio para fixa莽茫o](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/10?loc=ptbr)
+## [Question谩rio para fixa莽茫o](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/10?loc=ptbr)
## Revis茫o e Auto Aprendizagem
diff --git a/2-Regression/1-Tools/translations/README.pt.md b/2-Regression/1-Tools/translations/README.pt.md
index bc18ee02..948e3b32 100644
--- a/2-Regression/1-Tools/translations/README.pt.md
+++ b/2-Regression/1-Tools/translations/README.pt.md
@@ -5,7 +5,7 @@
> Sketchnote by [Tomomi Imura](https://www.twitter.com/girlie_mac)
-## [Question谩rio pr茅-palestra](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/9/)
+## [Question谩rio pr茅-palestra](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/9/)
> ### [Esta li莽茫o est谩 dispon铆vel em R!](./solution/R/lesson_1-R.ipynb)
@@ -202,7 +202,7 @@ Parab茅ns, constru铆ste o teu primeiro modelo linear de regress茫o, criaste uma
## 馃殌Challenge
Defina uma vari谩vel diferente deste conjunto de dados. Dica: edite esta linha:`X = X[:, np.newaxis, 2]`. Tendo em conta o objetivo deste conjunto de dados, o que 茅 que consegue descobrir sobre a progress茫o da diabetes como uma doen莽a?
-## [Question谩rio p贸s-palestra](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/10/)
+## [Question谩rio p贸s-palestra](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/10/)
## Review & Self Study
diff --git a/2-Regression/1-Tools/translations/README.tr.md b/2-Regression/1-Tools/translations/README.tr.md
index 561ab28b..93cb9263 100644
--- a/2-Regression/1-Tools/translations/README.tr.md
+++ b/2-Regression/1-Tools/translations/README.tr.md
@@ -4,7 +4,7 @@
> Sketchnote by [Tomomi Imura](https://www.twitter.com/girlie_mac)
-## [Ders 枚ncesi quiz](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/9/)
+## [Ders 枚ncesi quiz](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/9/)
> ### [R dili ile bu dersin i莽eri臒i!](././solution/R/lesson_1-R.ipynb)
@@ -197,7 +197,7 @@ Tebrikler, ilk do臒rusal regresyon modelinizi olu艧turdunuz, onunla bir tahmin o
## 馃殌Challenge
Bu veri k眉mesinden farkl谋 bir de臒i艧ken 莽izin. 陌pucu: bu sat谋r谋 d眉zenleyin: `X = X[:, np.newaxis, 2]`. Bu veri setinin hedefi g枚z 枚n眉ne al谋nd谋臒谋nda, diyabetin bir hastal谋k olarak ilerlemesi hakk谋nda neler ke艧fedebilirsiniz?
-## [Post-lecture quiz](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/10/)
+## [Post-lecture quiz](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/10/)
## 陌nceleme ve Bireysel 脟al谋艧ma
diff --git a/2-Regression/1-Tools/translations/README.zh-cn.md b/2-Regression/1-Tools/translations/README.zh-cn.md
index 2fc162d6..547681ec 100644
--- a/2-Regression/1-Tools/translations/README.zh-cn.md
+++ b/2-Regression/1-Tools/translations/README.zh-cn.md
@@ -4,7 +4,7 @@
> 浣滆€� [Tomomi Imura](https://www.twitter.com/girlie_mac)
-## [璇惧墠娴媇(https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/9/)
+## [璇惧墠娴媇(https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/9/)
## 浠嬬粛
鍦ㄨ繖鍥涜妭璇句腑锛屼綘灏嗕簡瑙ｅ浣曟瀯寤哄洖褰掓ā鍨嬨€傛垜浠皢寰堝揩璁ㄨ杩欎簺鏄粈涔堛€備絾鍦ㄤ綘鍋氫换浣曚簨鎯呬箣鍓嶏紝璇风‘淇濅綘鏈夊悎閫傜殑宸ュ叿鏉ュ紑濮嬭繖涓繃绋嬶紒
@@ -194,7 +194,7 @@ Scikit-learn 浣挎瀯寤烘ā鍨嬪拰璇勪及瀹冧滑鐨勪娇鐢ㄥ彉寰楃畝鍗曘€傚畠涓昏渚�
浠庤繖涓暟鎹泦涓粯鍒朵竴涓笉鍚岀殑鍙橀噺銆傛彁绀猴細缂栬緫杩欎竴琛岋細`X = X[:, np.newaxis, 2]`銆傞壌浜庢鏁版嵁闆嗙殑鐩爣锛屼綘鑳藉鍙戠幇绯栧翱鐥呬綔涓轰竴绉嶇柧鐥呯殑杩涘睍鎯呭喌鍚楋紵
-## [璇惧悗娴媇(https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/10/)
+## [璇惧悗娴媇(https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/10/)
## 澶嶄範涓庤嚜瀛�
diff --git a/2-Regression/1-Tools/translations/README.zh-tw.md b/2-Regression/1-Tools/translations/README.zh-tw.md
index 886aea2e..5af1d21e 100644
--- a/2-Regression/1-Tools/translations/README.zh-tw.md
+++ b/2-Regression/1-Tools/translations/README.zh-tw.md
@@ -4,7 +4,7 @@
> 浣滆€� [Tomomi Imura](https://www.twitter.com/girlie_mac)
-## [瑾插墠娓琞(https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/9/)
+## [瑾插墠娓琞(https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/9/)
## 浠嬬垂
@@ -195,7 +195,7 @@ Scikit-learn 浣挎寤烘ā鍨嬪拰瑭曚及瀹冨€戠殑浣跨敤璁婂緱绨″柈銆傚畠涓昏鍋�
寰為€欏€嬫暩鎿氶泦涓躬瑁戒竴鍊嬩笉鍚岀殑璁婇噺銆傛彁绀猴細绶ㄨ集閫欎竴琛岋細`X = X[:, np.newaxis, 2]`銆傞憭鏂兼鏁告摎闆嗙殑鐩锛屼綘鑳藉鐧肩従绯栧翱鐥呬綔鐐轰竴绋柧鐥呯殑閫插睍鎯呮硜鍡庯紵
-## [瑾插緦娓琞(https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/10/)
+## [瑾插緦娓琞(https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/10/)
## 寰╃繏鑸囪嚜瀛�
diff --git a/2-Regression/2-Data/README.md b/2-Regression/2-Data/README.md
index 939be63e..98235a70 100644
--- a/2-Regression/2-Data/README.md
+++ b/2-Regression/2-Data/README.md
@@ -4,7 +4,7 @@
Infographic by [Dasani Madipalli](https://twitter.com/dasani_decoded)
-## [Pre-lecture quiz](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/11/)
+## [Pre-lecture quiz](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/11/)
> ### [This lesson is available in R!](./solution/R/lesson_2-R.ipynb)
@@ -196,7 +196,7 @@ To get charts to display useful data, you usually need to group the data somehow
Explore the different types of visualization that Matplotlib offers. Which types are most appropriate for regression problems?
-## [Post-lecture quiz](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/12/)
+## [Post-lecture quiz](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/12/)
## Review & Self Study
diff --git a/2-Regression/2-Data/translations/README.es.md b/2-Regression/2-Data/translations/README.es.md
index 2c188cfe..7d5b6d20 100644
--- a/2-Regression/2-Data/translations/README.es.md
+++ b/2-Regression/2-Data/translations/README.es.md
@@ -4,7 +4,7 @@
Infograf铆a por [Dasani Madipalli](https://twitter.com/dasani_decoded)
-## [Examen previo a la lecci贸n](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/11?loc=es)
+## [Examen previo a la lecci贸n](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/11?loc=es)
> ### [Esta lecci贸n se encuentra disponible en R!](../solution/R/lesson_2-R.ipynb)
@@ -196,7 +196,7 @@ Para obtener gr谩ficas para mostrar datos 煤tiles, necesitas agrupar los datos d
Explora los distintos tipos de visualizaci贸n que ofrece Matplotlib. 驴Qu茅 tipos son los m谩s apropiados para problemas de regresi贸n?
-## [Examen posterior a la lecci贸n](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/12?loc=es)
+## [Examen posterior a la lecci贸n](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/12?loc=es)
## Revisi贸n y autoestudio
diff --git a/2-Regression/2-Data/translations/README.id.md b/2-Regression/2-Data/translations/README.id.md
index 9e0f05d6..8db14237 100644
--- a/2-Regression/2-Data/translations/README.id.md
+++ b/2-Regression/2-Data/translations/README.id.md
@@ -3,7 +3,7 @@
> Infografik oleh [Dasani Madipalli](https://twitter.com/dasani_decoded)
-## [Kuis pra-ceramah](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/11/)
+## [Kuis pra-ceramah](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/11/)
## Pembukaan
@@ -191,7 +191,7 @@ Untuk menjadikan sebuah grafik menjadi berguna, biasanya datanya harus dikelompo
Jelajahi jenis-jenis visualisasi yang beda dan yang disediakan Matplotlib. Jenis mana yang paling cocok untuk kasus regresi?
-## [Kuis pasca-ceramah](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/12/)
+## [Kuis pasca-ceramah](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/12/)
## Review & Pembelajaran Mandiri
diff --git a/2-Regression/2-Data/translations/README.it.md b/2-Regression/2-Data/translations/README.it.md
index d0f51a57..7fa06a3c 100644
--- a/2-Regression/2-Data/translations/README.it.md
+++ b/2-Regression/2-Data/translations/README.it.md
@@ -3,7 +3,7 @@
> 
> Infografica di [Dasani Madipalli](https://twitter.com/dasani_decoded)
-## [Quiz pre-lezione](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/11/?loc=it)
+## [Quiz pre-lezione](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/11/?loc=it)
## Introduzione
@@ -190,7 +190,7 @@ Per fare in modo che i grafici mostrino dati utili, di solito 猫 necessario ragg
Esplorare i diversi tipi di visualizzazione offerti da Matplotlib. Quali tipi sono pi霉 appropriati per i problemi di regressione?
-## [Quiz post-lezione](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/12/?loc=it)
+## [Quiz post-lezione](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/12/?loc=it)
## Revisione e Auto Apprendimento
diff --git a/2-Regression/2-Data/translations/README.ja.md b/2-Regression/2-Data/translations/README.ja.md
index a5f7dcf4..b780ec40 100644
--- a/2-Regression/2-Data/translations/README.ja.md
+++ b/2-Regression/2-Data/translations/README.ja.md
@@ -4,7 +4,7 @@
>
> [Dasani Madipalli](https://twitter.com/dasani_decoded) 銇倛銈嬨偆銉炽儠銈┿偘銉┿儠銈ｃ儍銈�
-## [璎涚京鍓嶃伄銈偆銈篯(https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/11?loc=ja)
+## [璎涚京鍓嶃伄銈偆銈篯(https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/11?loc=ja)
## 銈ゃ兂銉堛儹銉€銈偡銉с兂
@@ -195,7 +195,7 @@ Jupyter notebook銇с亞銇俱亸鍒╃敤銇с亶銈嬨儐銉笺偪鍙鍖栥儵銈ゃ儢銉┿儶銇�
Matplotlib銇屾彁渚涖仚銈嬫銆呫仾銈裤偆銉椼伄銉撱偢銉ャ偄銉┿偆銈笺兗銈枫儳銉炽倰鎺€仯銇︺伩銇俱仐銈囥亞銆傚洖甯般伄鍟忛銇伅銇┿伄銈裤偆銉椼亴鏈€銈傞仼銇椼仸銇勩倠銇с仐銈囥亞銇嬶紵
-## [璎涚京寰屻偗銈ゃ偤](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/12?loc=ja)
+## [璎涚京寰屻偗銈ゃ偤](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/12?loc=ja)
## 銉儞銉ャ兗 & 鑷富瀛︾繏
diff --git a/2-Regression/2-Data/translations/README.ko.md b/2-Regression/2-Data/translations/README.ko.md
index 5bf393f7..cc3ae37a 100644
--- a/2-Regression/2-Data/translations/README.ko.md
+++ b/2-Regression/2-Data/translations/README.ko.md
@@ -4,7 +4,7 @@
> Infographic by [Dasani Madipalli](https://twitter.com/dasani_decoded)
-## [臧曥潣 鞝� 韤挫](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/11/)
+## [臧曥潣 鞝� 韤挫](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/11/)
## 靻岅皽
@@ -191,7 +191,7 @@ Jupyter notebooks鞐愳劀 鞛� 鞛戨彊頃橂姅 雿办澊韯� 鞁滉皝頇� 霛检澊敫岆煬毽姅
Matplotlib鞐愳劀 鞝滉车頃橂姅 雼れ枒頃� 鞁滉皝頇� 韮€鞛呾潉 彀眷晞氤挫劯鞖�. regression 氍胳牅鞐� 臧€鞛� 鞝侂嫻頃� 韮€鞛呾潃 氍挫棁鞚戈皜鞖�?
-## [臧曥潣 頉� 韤挫](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/12/)
+## [臧曥潣 頉� 韤挫](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/12/)
## 瓴€韱� & 鞛愱赴欤茧弰 頃欖姷
diff --git a/2-Regression/2-Data/translations/README.pt-br.md b/2-Regression/2-Data/translations/README.pt-br.md
index 7ba9fe85..0dbade75 100644
--- a/2-Regression/2-Data/translations/README.pt-br.md
+++ b/2-Regression/2-Data/translations/README.pt-br.md
@@ -4,7 +4,7 @@
Infogr谩fico por [Dasani Madipalli](https://twitter.com/dasani_decoded)
-## [Question谩rio inicial](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/11?loc=ptbr)
+## [Question谩rio inicial](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/11?loc=ptbr)
> ### [Esta li莽ao est谩 dispon铆vel em R!](../solution/R/lesson_2-R.ipynb)
@@ -197,7 +197,7 @@ Para fazer com que os gr谩ficos exibam dados 煤teis, voc锚 precisa agrupar os da
Explore os diferentes tipos de visualiza莽茫o que o Matplotlib oferece. Quais tipos s茫o mais adequados para problemas de regress茫o?
-## [Question谩rio para fixa莽茫o](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/12?loc=ptbr)
+## [Question谩rio para fixa莽茫o](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/12?loc=ptbr)
## Revis茫o e Auto Aprendizagem
diff --git a/2-Regression/2-Data/translations/README.pt.md b/2-Regression/2-Data/translations/README.pt.md
index e2869a15..4ede28b0 100644
--- a/2-Regression/2-Data/translations/README.pt.md
+++ b/2-Regression/2-Data/translations/README.pt.md
@@ -4,7 +4,7 @@
Infographic by [Dasani Madipalli](https://twitter.com/dasani_decoded)
-## [Teste de pr茅-aula](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/11/)
+## [Teste de pr茅-aula](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/11/)
> ### [Esta li莽茫o est谩 dispon铆vel em R!](./solution/R/lesson_2-R.ipynb)
@@ -196,7 +196,7 @@ Esta 茅 uma visualiza莽茫o de dados mais 煤til! Parece indicar que o pre莽o mais
Explore os diferentes tipos de visualiza莽茫o que o Matplotlib oferece. Que tipos s茫o mais apropriados para problemas de regress茫o?
-## [Question谩rio p贸s-palestra](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/12/)
+## [Question谩rio p贸s-palestra](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/12/)
## Revis茫o e Estudo Autom谩tico
diff --git a/2-Regression/2-Data/translations/README.zh-cn.md b/2-Regression/2-Data/translations/README.zh-cn.md
index f204e1e7..3ee2d442 100644
--- a/2-Regression/2-Data/translations/README.zh-cn.md
+++ b/2-Regression/2-Data/translations/README.zh-cn.md
@@ -3,7 +3,7 @@
> 浣滆€� [Dasani Madipalli](https://twitter.com/dasani_decoded)
-## [璇惧墠娴媇(https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/11/)
+## [璇惧墠娴媇(https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/11/)
## 浠嬬粛
@@ -192,7 +192,7 @@
鎺㈢储 Matplotlib 鎻愪緵鐨勪笉鍚岀被鍨嬬殑鍙鍖栥€傚摢绉嶇被鍨嬫渶閫傚悎鍥炲綊闂锛�
-## [璇惧悗娴媇(https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/12/)
+## [璇惧悗娴媇(https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/12/)
## 澶嶄範涓庤嚜瀛�
diff --git a/2-Regression/2-Data/translations/README.zh-tw.md b/2-Regression/2-Data/translations/README.zh-tw.md
index f9bfbb99..2ec6a83f 100644
--- a/2-Regression/2-Data/translations/README.zh-tw.md
+++ b/2-Regression/2-Data/translations/README.zh-tw.md
@@ -3,7 +3,7 @@
> 浣滆€� [Dasani Madipalli](https://twitter.com/dasani_decoded)
-## [瑾插墠娓琞(https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/11/)
+## [瑾插墠娓琞(https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/11/)
## 浠嬬垂
@@ -192,7 +192,7 @@
鎺㈢储 Matplotlib 鎻愪緵鐨勪笉鍚岄鍨嬬殑鍙鍖栥€傚摢绋鍨嬫渶閬╁悎鍥炴鍟忛锛�
-## [瑾插緦娓琞(https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/12/)
+## [瑾插緦娓琞(https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/12/)
## 寰╃繏鑸囪嚜瀛�
diff --git a/2-Regression/3-Linear/README.md b/2-Regression/3-Linear/README.md
index b7e07bac..63596de4 100644
--- a/2-Regression/3-Linear/README.md
+++ b/2-Regression/3-Linear/README.md
@@ -2,7 +2,7 @@
> Infographic by [Dasani Madipalli](https://twitter.com/dasani_decoded)
-## [Pre-lecture quiz](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/13/)
+## [Pre-lecture quiz](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/13/)
> ### [This lesson is available in R!](./solution/R/lesson_3-R.ipynb)
### Introduction
@@ -326,7 +326,7 @@ This should give us the best determination coefficient of almost 97%, and MSE=2.
Test several different variables in this notebook to see how correlation corresponds to model accuracy.
-## [Post-lecture quiz](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/14/)
+## [Post-lecture quiz](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/14/)
## Review & Self Study
diff --git a/2-Regression/3-Linear/solution/R/lesson_3-R.ipynb b/2-Regression/3-Linear/solution/R/lesson_3-R.ipynb
index ba156fb6..e6a4b71c 100644
--- a/2-Regression/3-Linear/solution/R/lesson_3-R.ipynb
+++ b/2-Regression/3-Linear/solution/R/lesson_3-R.ipynb
@@ -1058,7 +1058,7 @@
"\n",
"Test several different variables in this notebook to see how correlation corresponds to model accuracy.\n",
"\n",
- "## [**Post-lecture quiz**](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/14/)\n",
+ "## [**Post-lecture quiz**](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/14/)\n",
"\n",
"## **Review & Self Study**\n",
"\n",
diff --git a/2-Regression/3-Linear/solution/R/lesson_3.Rmd b/2-Regression/3-Linear/solution/R/lesson_3.Rmd
index 712011f4..50d4c134 100644
--- a/2-Regression/3-Linear/solution/R/lesson_3.Rmd
+++ b/2-Regression/3-Linear/solution/R/lesson_3.Rmd
@@ -662,7 +662,7 @@ The `polynomial model` prediction does make sense, given the scatter plots of `p
Test several different variables in this notebook to see how correlation corresponds to model accuracy.
-## [**Post-lecture quiz**](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/14/)
+## [**Post-lecture quiz**](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/14/)
## **Review & Self Study**
diff --git a/2-Regression/3-Linear/translations/README.es.md b/2-Regression/3-Linear/translations/README.es.md
index db79c5af..ecc3a95e 100644
--- a/2-Regression/3-Linear/translations/README.es.md
+++ b/2-Regression/3-Linear/translations/README.es.md
@@ -3,7 +3,7 @@
> Infograf铆a de [Dasani Madipalli](https://twitter.com/dasani_decoded)
-## [Examen previo a la lecci贸n](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/13?loc=es)
+## [Examen previo a la lecci贸n](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/13?loc=es)
> ### [隆Esta lecci贸n est谩 disponible en R!](../solution/R/lesson_3-R.ipynb)
@@ -331,7 +331,7 @@ Llama a `predict()` para hacer una predicci贸n:
Prueba variables diferentes en este notebook para ver c贸mo la correlaci贸n corresponde a la precisi贸n del modelo.
-## [Examen posterior a la lecci贸n](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/14?loc=es)
+## [Examen posterior a la lecci贸n](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/14?loc=es)
## Revisi贸n y auto-estudio
diff --git a/2-Regression/3-Linear/translations/README.id.md b/2-Regression/3-Linear/translations/README.id.md
index b454c4db..66c8908b 100644
--- a/2-Regression/3-Linear/translations/README.id.md
+++ b/2-Regression/3-Linear/translations/README.id.md
@@ -2,7 +2,7 @@
> Infografik oleh [Dasani Madipalli](https://twitter.com/dasani_decoded)
-## [Kuis pra-ceramah](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/13/)
+## [Kuis pra-ceramah](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/13/)
### Pembukaan
Selama ini kamu telah menjelajahi apa regresi itu dengan data contoh yang dikumpulkan dari *dataset* harga labu yang kita akan gunakan terus sepanjang pelajaran ini. Kamu juga telah memvisualisasikannya dengan Matplotlib.
@@ -324,7 +324,7 @@ Itu sangat masuk akal dengan bagan sebelumnya! Selain itu, jika ini model lebih
Coba-cobalah variabel-variabel yang lain di *notebook* ini untuk melihat bagaimana korelasi berhubungan dengan akurasi model.
-## [Kuis pasca-ceramah](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/14/)
+## [Kuis pasca-ceramah](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/14/)
## Review & Pembelajaran Mandiri
diff --git a/2-Regression/3-Linear/translations/README.it.md b/2-Regression/3-Linear/translations/README.it.md
index f2c0a295..dce65d9a 100644
--- a/2-Regression/3-Linear/translations/README.it.md
+++ b/2-Regression/3-Linear/translations/README.it.md
@@ -3,7 +3,7 @@
> Infografica di [Dasani Madipalli](https://twitter.com/dasani_decoded)
-## [Quiz pre-lezione](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/13/?loc=it)
+## [Quiz pre-lezione](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/13/?loc=it)
### Introduzione
@@ -328,7 +328,7 @@ Ben fatto! Sono stati creati due modelli di regressione in una lezione. Nella s
Testare diverse variabili in questo notebook per vedere come la correlazione corrisponde all'accuratezza del modello.
-## [Quiz post-lezione](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/14/?loc=it)
+## [Quiz post-lezione](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/14/?loc=it)
## Revisione e Auto Apprendimento
diff --git a/2-Regression/3-Linear/translations/README.ja.md b/2-Regression/3-Linear/translations/README.ja.md
index 0bebdb96..8a99b71a 100644
--- a/2-Regression/3-Linear/translations/README.ja.md
+++ b/2-Regression/3-Linear/translations/README.ja.md
@@ -2,7 +2,7 @@
 銇倛銈嬨偆銉炽儠銈┿偘銉┿儠銈ｃ儍銈�
-## [璎涚京鍓嶃伄銈偆銈篯(https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/13/)
+## [璎涚京鍓嶃伄銈偆銈篯(https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/13/)
### 銈ゃ兂銉堛儹銉€銈偡銉с兂
銇撱倢銇俱仹銆併亾銇儸銉冦偣銉炽仹浣跨敤銇欍倠銈儨銉併儯銇尽鏍笺儑銉笺偪銈汇儍銉堛亱銈夐泦銈併仧銈点兂銉椼儷銉囥兗銈裤倰浣裤仯銇︺€佸洖甯般仺銇綍銇嬨倰鎺€仯銇︺亶銇俱仐銇熴€傘伨銇熴€丮atplotlib銈掍娇銇ｃ仸鍙鍖栥倰琛屻亜銇俱仐銇熴€�
@@ -323,7 +323,7 @@ Scikit-learn銇伅銆佸闋呭紡鍥炲赴銉€儑銉倰妲嬬瘔銇欍倠銇熴倎銇究鍒┿仾AP
銇撱伄銉庛兗銉堛儢銉冦偗銇с亜銇忋仱銇嬨伄鐣般仾銈嬪鏁般倰銉嗐偣銉堛仐銆佺浉闁㈤枹淇傘亴銉€儑銉伄绮惧害銇仼銇倛銇嗐伀褰遍熆銇欍倠銇嬨倰纰鸿獚銇椼仸銇裤仸銇忋仩銇曘亜銆�
-## [璎涚京寰屻偗銈ゃ偤](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/14/)
+## [璎涚京寰屻偗銈ゃ偤](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/14/)
## 銉儞銉ャ兗 & 鑷富瀛︾繏
diff --git a/2-Regression/3-Linear/translations/README.ko.md b/2-Regression/3-Linear/translations/README.ko.md
index baa9abf8..de898562 100644
--- a/2-Regression/3-Linear/translations/README.ko.md
+++ b/2-Regression/3-Linear/translations/README.ko.md
@@ -3,7 +3,7 @@
> Infographic by [Dasani Madipalli](https://twitter.com/dasani_decoded)
-## [臧曥潣 鞝� 韤挫](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/13/)
+## [臧曥潣 鞝� 韤挫](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/13/)
### 靻岅皽
@@ -327,7 +327,7 @@ Scikit-learn鞐愲姅 polynomial regression 氇嵏鞚� 毵岆摛 霑� 霃勳泙鞚� 氚涭潉
雲疙姼攵侅棎靹� 雼るジ 氤€靾橂ゼ 韰岇姢韸疙晿氅挫劀 靸侁磤 甏€瓿勱皜 氇嵏 鞝曧檿霃勳棎 鞏措柣瓴� 雽€鞚戨悩電� 歆€ 氪呺媹雼�.
-## [臧曥潣 頉� 韤挫](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/14/)
+## [臧曥潣 頉� 韤挫](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/14/)
## 瓴€韱� & 鞛愱赴欤茧弰 頃欖姷
diff --git a/2-Regression/3-Linear/translations/README.pt-br.md b/2-Regression/3-Linear/translations/README.pt-br.md
index 81b137c7..fe3deadc 100644
--- a/2-Regression/3-Linear/translations/README.pt-br.md
+++ b/2-Regression/3-Linear/translations/README.pt-br.md
@@ -3,7 +3,7 @@
> Infogr谩fico por [Dasani Madipalli](https://twitter.com/dasani_decoded)
-## [Question谩rio inicial](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/13?loc=ptbr)
+## [Question谩rio inicial](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/13?loc=ptbr)
> ### [Esta li莽ao est谩 dispon铆vel em R!](../solution/R/lesson_3-R.ipynb)
@@ -331,7 +331,7 @@ E se esse modelo for melhor que o anterior usando o mesmo conjunto de dados, voc
Teste vari谩veis diferentes neste _notebook_ para ver como a correla莽茫o corresponde 脿 acur谩cia do modelo.
-## [Question谩rio para fixa莽茫o](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/14?loc=ptbr)
+## [Question谩rio para fixa莽茫o](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/14?loc=ptbr)
## Revis茫o e Auto Aprendizagem
diff --git a/2-Regression/3-Linear/translations/README.pt.md b/2-Regression/3-Linear/translations/README.pt.md
index 68f62714..c778e031 100644
--- a/2-Regression/3-Linear/translations/README.pt.md
+++ b/2-Regression/3-Linear/translations/README.pt.md
@@ -2,7 +2,7 @@
> Infogr谩fico de [Dasani Madipalli](https://twitter.com/dasani_decoded)
-## [Question谩rio pr茅-sele莽茫o](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/13/)
+## [Question谩rio pr茅-sele莽茫o](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/13/)
> ### [Esta li莽茫o est谩 dispon铆vel em R!](./solution/R/lesson_3-R.ipynb)
### Introdu莽茫o
@@ -321,7 +321,7 @@ Faz sentido, dado o enredo! E, se este 茅 um modelo melhor do que o anterior, ol
## 馃殌 desafio
Teste v谩rias vari谩veis diferentes neste bloco de notas para ver como a correla莽茫o corresponde 脿 precis茫o do modelo.
-##[Question谩rio p贸s-palestra](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/14/)
+##[Question谩rio p贸s-palestra](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/14/)
## Revis茫o e Estudo Autom谩tico
diff --git a/2-Regression/3-Linear/translations/README.zh-cn.md b/2-Regression/3-Linear/translations/README.zh-cn.md
index 94e59545..da5e02ad 100644
--- a/2-Regression/3-Linear/translations/README.zh-cn.md
+++ b/2-Regression/3-Linear/translations/README.zh-cn.md
@@ -3,7 +3,7 @@
> 浣滆€� [Dasani Madipalli](https://twitter.com/dasani_decoded)
-## [璇惧墠娴媇(https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/13/)
+## [璇惧墠娴媇(https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/13/)
### 浠嬬粛
@@ -330,7 +330,7 @@ Scikit-learn 鍖呭惈涓€涓敤浜庢瀯寤哄椤瑰紡鍥炲綊妯″瀷鐨勬湁鐢� API - `make_
鍦ㄦ notebook 涓祴璇曞嚑涓笉鍚岀殑鍙橀噺锛屼互鏌ョ湅鐩稿叧鎬т笌妯″瀷鍑嗙‘鎬х殑瀵瑰簲鍏崇郴銆�
-## [璇惧悗娴媇(https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/14/)
+## [璇惧悗娴媇(https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/14/)
## 澶嶄範涓庤嚜瀛�
diff --git a/2-Regression/3-Linear/translations/README.zh-tw.md b/2-Regression/3-Linear/translations/README.zh-tw.md
index 4592c2f1..b57e4a90 100644
--- a/2-Regression/3-Linear/translations/README.zh-tw.md
+++ b/2-Regression/3-Linear/translations/README.zh-tw.md
@@ -4,7 +4,7 @@
> 浣滆€� [Dasani Madipalli](https://twitter.com/dasani_decoded)
-## [瑾插墠娓琞(https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/13/)
+## [瑾插墠娓琞(https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/13/)
### 浠嬬垂
@@ -331,7 +331,7 @@ Scikit-learn 鍖呭惈涓€鍊嬬敤鏂兼寤哄闋呭紡鍥炴妯″瀷鐨勬湁鐢� API - `make_
鍦ㄦ notebook 涓脯瑭﹀咕鍊嬩笉鍚岀殑璁婇噺锛屼互鏌ョ湅鐩搁棞鎬ц垏妯″瀷婧栫⒑鎬х殑灏嶆噳闂滅郴銆�
-## [瑾插緦娓琞(https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/14/)
+## [瑾插緦娓琞(https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/14/)
## 寰╃繏鑸囪嚜瀛�
diff --git a/2-Regression/4-Logistic/README.md b/2-Regression/4-Logistic/README.md
index 6da94915..8bfd426e 100644
--- a/2-Regression/4-Logistic/README.md
+++ b/2-Regression/4-Logistic/README.md
@@ -2,7 +2,7 @@
> Infographic by [Dasani Madipalli](https://twitter.com/dasani_decoded)
-## [Pre-lecture quiz](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/15/)
+## [Pre-lecture quiz](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/15/)
> ### [This lesson is available in R!](./solution/R/lesson_4-R.ipynb)
@@ -298,7 +298,7 @@ In future lessons on classifications, you will learn how to iterate to improve y
There's a lot more to unpack regarding logistic regression! But the best way to learn is to experiment. Find a dataset that lends itself to this type of analysis and build a model with it. What do you learn? tip: try [Kaggle](https://www.kaggle.com/search?q=logistic+regression+datasets) for interesting datasets.
-## [Post-lecture quiz](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/16/)
+## [Post-lecture quiz](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/16/)
## Review & Self Study
diff --git a/2-Regression/4-Logistic/solution/R/lesson_4-R.ipynb b/2-Regression/4-Logistic/solution/R/lesson_4-R.ipynb
index c82e79f1..7770d7d9 100644
--- a/2-Regression/4-Logistic/solution/R/lesson_4-R.ipynb
+++ b/2-Regression/4-Logistic/solution/R/lesson_4-R.ipynb
@@ -45,7 +45,7 @@
{
"cell_type": "markdown",
"source": [
- "#### ** [Pre-lecture quiz](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/15/)**\n",
+ "#### ** [Pre-lecture quiz](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/15/)**\n",
"\n",
"#### Introduction\n",
"\n",
diff --git a/2-Regression/4-Logistic/solution/R/lesson_4.Rmd b/2-Regression/4-Logistic/solution/R/lesson_4.Rmd
index 26ac170c..4f0b161f 100644
--- a/2-Regression/4-Logistic/solution/R/lesson_4.Rmd
+++ b/2-Regression/4-Logistic/solution/R/lesson_4.Rmd
@@ -14,7 +14,7 @@ output:
{width="600"}
-#### ** [Pre-lecture quiz](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/15/)**
+#### ** [Pre-lecture quiz](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/15/)**
#### Introduction
diff --git a/2-Regression/4-Logistic/translations/README.es.md b/2-Regression/4-Logistic/translations/README.es.md
index d0e9913d..188ab653 100644
--- a/2-Regression/4-Logistic/translations/README.es.md
+++ b/2-Regression/4-Logistic/translations/README.es.md
@@ -3,7 +3,7 @@
> Infograf铆a de [Dasani Madipalli](https://twitter.com/dasani_decoded)
-## [Examen previo a la lecci贸n](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/15?loc=es)
+## [Examen previo a la lecci贸n](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/15?loc=es)
> ### [Esta lecci贸n se encuentra disponible en R!](../solution/R/lesson_4-R.ipynb)
@@ -302,7 +302,7 @@ En futuras lecciones de clasificaci贸n, aprender谩s c贸mo iterar para mejorar lo
隆Hay mucho m谩s para desempacar respecto a la regresi贸n log铆stica! Pero la mejor forma de aprender es experimentar. Encuentra un conjunto de datos que se preste para este tipo de an谩lisis y construye un modelo con 茅l. 驴Qu茅 aprendes? tipo: prueba [Kaggle](https://www.kaggle.com/search?q=logistic+regression+datasets) por conjuntos de datos interesantes.
-## [Examen posterior a la lecci贸n](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/16?loc=es)
+## [Examen posterior a la lecci贸n](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/16?loc=es)
## Revisi贸n & autoestudio
diff --git a/2-Regression/4-Logistic/translations/README.id.md b/2-Regression/4-Logistic/translations/README.id.md
index 5ec38e2d..cb7877b5 100644
--- a/2-Regression/4-Logistic/translations/README.id.md
+++ b/2-Regression/4-Logistic/translations/README.id.md
@@ -3,7 +3,7 @@
> Infografik oleh [Dasani Madipalli](https://twitter.com/dasani_decoded)
-## [Kuis pra-ceramah](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/15/)
+## [Kuis pra-ceramah](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/15/)
## Pembukaan
@@ -291,7 +291,7 @@ Nanti dalam pelajaran lebih lanjut tentang klasifikasi, kamu akan belajar bagaim
Masih ada banyak tentang regresi logistik! Tetapi cara paling baik adalah untuk bereksperimen. Carilah sebuah *dataset* yang bisa diteliti seperti ini dan bangunlah sebuah model darinya. Apa yang kamu pelajari? Petunjuk: Coba [Kaggle](https://kaggle.com) untuk *dataset-dataset* menarik.
-## [Kuis pasca-ceramah](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/16/)
+## [Kuis pasca-ceramah](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/16/)
## Review & Pembelajaran mandiri
diff --git a/2-Regression/4-Logistic/translations/README.it.md b/2-Regression/4-Logistic/translations/README.it.md
index c4e26979..0b60097d 100644
--- a/2-Regression/4-Logistic/translations/README.it.md
+++ b/2-Regression/4-Logistic/translations/README.it.md
@@ -3,7 +3,7 @@
> Infografica di [Dasani Madipalli](https://twitter.com/dasani_decoded)
-## [Quiz pre-lezione](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/15/?loc=it)
+## [Quiz pre-lezione](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/15/?loc=it)
## Introduzione
@@ -284,7 +284,7 @@ Nelle lezioni future sulle classificazioni si imparer脿 come eseguire l'iterazio
C'猫 molto altro da svelare riguardo alla regressione logistica! Ma il modo migliore per imparare 猫 sperimentare. Trovare un insieme di dati che si presti a questo tipo di analisi e costruire un modello con esso. Cosa si 猫 appreso? suggerimento: provare [Kaggle](https://kaggle.com) per ottenere insiemi di dati interessanti.
-## [Quiz post-lezione](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/16/?loc=it)
+## [Quiz post-lezione](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/16/?loc=it)
## Revisione e Auto Apprendimento
diff --git a/2-Regression/4-Logistic/translations/README.ja.md b/2-Regression/4-Logistic/translations/README.ja.md
index 0158d9ed..0b6b123d 100644
--- a/2-Regression/4-Logistic/translations/README.ja.md
+++ b/2-Regression/4-Logistic/translations/README.ja.md
@@ -2,7 +2,7 @@
 銇倛銈嬨偆銉炽儠銈┿偘銉┿儠銈ｃ儍銈�
-## [璎涚京鍓嶃伄銈偆銈篯(https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/15/)
+## [璎涚京鍓嶃伄銈偆銈篯(https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/15/)
## 銈ゃ兂銉堛儹銉€銈偡銉с兂
@@ -299,7 +299,7 @@ print(auc)
銉偢銈广儐銈ｃ儍銈洖甯般伀銇ゃ亜銇︺伅銆併伨銇犮伨銇犺В銇嶆槑銇嬨仚銇广亶銇撱仺銇屻仧銇忋仌銈撱亗銈娿伨銇欍€傘仐銇嬨仐銆佸銇躲仧銈併伄鏈€鑹伄鏂规硶銇€佸疅楱撱仚銈嬨亾銇ㄣ仹銇欍€傘亾銇ó銇垎鏋愩伀閬┿仐銇熴儑銉笺偪銈汇儍銉堛倰瑕嬨仱銇戙仸銆併仢銈屻倰浣裤仯銇︺儮銉囥儷銈掓绡夈仐銇︺伩銇俱仐銈囥亞銆傘儝銉炽儓锛氶潰鐧姐亜銉囥兗銈裤偦銉冦儓銈掓帰銇欍仧銈併伀[Kaggle](https://www.kaggle.com/search?q=logistic+regression+datasets) 銈掕│銇椼仸銇裤仸銇忋仩銇曘亜銆�
-## [璎涚京寰屻偗銈ゃ偤](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/16/)
+## [璎涚京寰屻偗銈ゃ偤](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/16/)
## 銉儞銉ャ兗 & 鑷富瀛︾繏
diff --git a/2-Regression/4-Logistic/translations/README.ko.md b/2-Regression/4-Logistic/translations/README.ko.md
index 5a6ac4a3..44ab6ecc 100644
--- a/2-Regression/4-Logistic/translations/README.ko.md
+++ b/2-Regression/4-Logistic/translations/README.ko.md
@@ -3,7 +3,7 @@
> Infographic by [Dasani Madipalli](https://twitter.com/dasani_decoded)
-## [臧曥潣 鞝� 韤挫](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/15/)
+## [臧曥潣 鞝� 韤挫](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/15/)
## 靻岅皽
@@ -300,7 +300,7 @@ classifications鞐� 雽€頃� 鞚错泟 臧曥潣鞐愳劀, 氇嵏鞚� 鞀れ綌鞏措ゼ 臧滌劆頃�
logistic regression瓿� 甏€霠暣靹� 頀€鞏挫暭頃� 雮挫毄鞚� 雿� 鞛堨姷雼堧嫟! 頃橃毵� 氚办毎旮� 膦嬱潃 氚╈嫕鞚€ 鞁ろ棙鞛呺媹雼�. 鞚措煱 攵勳劃鞐� 鞝侂嫻頃� 雿办澊韯办厠鞚� 彀眷晞靹� 氇嵏鞚� 毵岆摥雼堧嫟. 氍挫棁鞚� 氚办毎雮橃殧? 韺�: 頋ル搿滌毚 雿办澊韯办厠鞙茧 [Kaggle](https://www.kaggle.com/search?q=logistic+regression+datasets)鞐愳劀 鞁滊弰頃措炒靹胳殧.
-## [臧曥潣 頉� 韤挫](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/16/)
+## [臧曥潣 頉� 韤挫](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/16/)
## 瓴€韱� & 鞛愱赴欤茧弰 頃欖姷
diff --git a/2-Regression/4-Logistic/translations/README.pt-br.md b/2-Regression/4-Logistic/translations/README.pt-br.md
index b62f4a33..70f1881b 100644
--- a/2-Regression/4-Logistic/translations/README.pt-br.md
+++ b/2-Regression/4-Logistic/translations/README.pt-br.md
@@ -2,7 +2,7 @@
> Infogr谩fico por [Dasani Madipalli](https://twitter.com/dasani_decoded)
-## [Question谩rio inicial](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/15?loc=ptbr)
+## [Question谩rio inicial](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/15?loc=ptbr)
> ### [Esta li莽ao est谩 dispon铆vel em R!](../solution/R/lesson_4-R.ipynb)
@@ -300,7 +300,7 @@ Em outras li莽玫es sobre classifica莽茫o, voc锚 aprender谩 como iterar para melh
Ainda h谩 muito sobre regress茫o log铆stica! E a melhor maneira de aprender 茅 experimentando. Encontre um conjunto de dados para este tipo de an谩lise e construa um modelo com ele. O que voc锚 aprendeu? dica: tente o [Kaggle](https://www.kaggle.com/search?q=logistic+regression+datasets) para conjuntos de dados interessantes.
-## [Question谩rio para fixa莽茫o](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/16?loc=ptbr)
+## [Question谩rio para fixa莽茫o](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/16?loc=ptbr)
## Revis茫o e Auto Aprendizagem
diff --git a/2-Regression/4-Logistic/translations/README.pt.md b/2-Regression/4-Logistic/translations/README.pt.md
index 82929a23..21e264db 100644
--- a/2-Regression/4-Logistic/translations/README.pt.md
+++ b/2-Regression/4-Logistic/translations/README.pt.md
@@ -2,7 +2,7 @@
> Infographic by [Dasani Madipalli](https://twitter.com/dasani_decoded)
-## [Question谩rio pr茅-palestra](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/15/)
+## [Question谩rio pr茅-palestra](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/15/)
> ### [Esta li莽茫o est谩 dispon铆vel em R!](./solution/R/lesson_4-R.ipynb)
@@ -291,7 +291,7 @@ Em li莽玫es futuras sobre classifica莽玫es, voc锚 aprender谩 a iterar para melho
H谩 muito mais a desempacotar em rela莽茫o 脿 regress茫o log铆stica! Mas a melhor maneira de aprender 茅 experimentar. Encontre um conjunto de dados que se preste a esse tipo de an谩lise e construa um modelo com ele. O que voc锚 aprende? dica: tente [Kaggle](https://www.kaggle.com/search?q=logistic+regression+datasets) para obter conjuntos de dados interessantes.
-## [Teste p贸s-aula](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/16/)
+## [Teste p贸s-aula](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/16/)
## An谩lise e autoestudo
diff --git a/2-Regression/4-Logistic/translations/README.zh-cn.md b/2-Regression/4-Logistic/translations/README.zh-cn.md
index fb80b284..c23d60e4 100644
--- a/2-Regression/4-Logistic/translations/README.zh-cn.md
+++ b/2-Regression/4-Logistic/translations/README.zh-cn.md
@@ -3,7 +3,7 @@
> 浣滆€� [Dasani Madipalli](https://twitter.com/dasani_decoded)
-## [璇惧墠娴媇(https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/15/)
+## [璇惧墠娴媇(https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/15/)
## 浠嬬粛
@@ -289,7 +289,7 @@ print(auc)
鍏充簬閫昏緫鍥炲綊锛岃繕鏈夊緢澶氫笢瑗块渶瑕佽В寮€锛佷絾鏈€濂界殑瀛︿範鏂规硶鏄疄楠屻€傛壘鍒伴€傚悎姝ょ被鍒嗘瀽鐨勬暟鎹泦骞剁敤瀹冩瀯寤烘ā鍨嬨€備綘瀛﹀埌浜嗕粈涔堬紵灏忚创澹細灏濊瘯 [Kaggle](https://kaggle.com) 鑾峰彇鏈夎叮鐨勬暟鎹泦銆�
-## [璇惧悗娴媇(https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/16/)
+## [璇惧悗娴媇(https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/16/)
## 澶嶄範涓庤嚜瀛�
diff --git a/2-Regression/4-Logistic/translations/README.zh-tw.md b/2-Regression/4-Logistic/translations/README.zh-tw.md
index ab481098..6eef6e5f 100644
--- a/2-Regression/4-Logistic/translations/README.zh-tw.md
+++ b/2-Regression/4-Logistic/translations/README.zh-tw.md
@@ -4,7 +4,7 @@
> 浣滆€� [Dasani Madipalli](https://twitter.com/dasani_decoded)
-## [瑾插墠娓琞(https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/15/)
+## [瑾插墠娓琞(https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/15/)
## 浠嬬垂
@@ -290,7 +290,7 @@ print(auc)
闂滄柤閭忚集鍥炴锛岄倓鏈夊緢澶氭澅瑗块渶瑕佽В闁嬶紒浣嗘渶濂界殑瀛哥繏鏂规硶鏄椹椼€傛壘鍒伴仼鍚堟椤炲垎鏋愮殑鏁告摎闆嗕甫鐢ㄥ畠妲嬪缓妯″瀷銆備綘瀛稿埌浜嗕粈楹斤紵灏忚布澹細鍢楄│ [Kaggle](https://kaggle.com) 鐛插彇鏈夎叮鐨勬暩鎿氶泦銆�
-## [瑾插緦娓琞(https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/16/)
+## [瑾插緦娓琞(https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/16/)
## 寰╃繏鑸囪嚜瀛�
diff --git a/3-Web-App/1-Web-App/README.md b/3-Web-App/1-Web-App/README.md
index 7af22fca..cc9790ee 100644
--- a/3-Web-App/1-Web-App/README.md
+++ b/3-Web-App/1-Web-App/README.md
@@ -11,7 +11,7 @@ We will continue our use of notebooks to clean data and train our model, but you
To do this, you need to build a web app using Flask.
-## [Pre-lecture quiz](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/17/)
+## [Pre-lecture quiz](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/17/)
## Building an app
@@ -334,7 +334,7 @@ In a professional setting, you can see how good communication is necessary betwe
Instead of working in a notebook and importing the model to the Flask app, you could train the model right within the Flask app! Try converting your Python code in the notebook, perhaps after your data is cleaned, to train the model from within the app on a route called `train`. What are the pros and cons of pursuing this method?
-## [Post-lecture quiz](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/18/)
+## [Post-lecture quiz](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/18/)
## Review & Self Study
diff --git a/3-Web-App/1-Web-App/translations/README.es.md b/3-Web-App/1-Web-App/translations/README.es.md
index 5eb396b4..c98a8fcf 100644
--- a/3-Web-App/1-Web-App/translations/README.es.md
+++ b/3-Web-App/1-Web-App/translations/README.es.md
@@ -11,7 +11,7 @@ Continuaremos nuestro uso de notebooks para limpiar los datos y entrenar nuestro
Para hacer esto, necesitas construir una aplicaci贸n web usando Flask.
-## [Examen previo a la lecci贸n](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/17?loc=es)
+## [Examen previo a la lecci贸n](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/17?loc=es)
## Construyendo una aplicaci贸n
@@ -335,7 +335,7 @@ En un entorno profesional, puedes ver c贸mo la buena comunicaci贸n es necesaria
En lugar de trabajar en un notebook e importar el modelo a una aplicaci贸n Flask, 隆podr铆as entrenar el modelo directo en la aplicaci贸n Flask! Intenta convertir tu c贸digo Python en el notebook, quiz谩 despu茅s que tus datos sean limpiados, para entrenar el modelo desde la aplicaci贸n en una ruta llamada `train`. 驴Cu谩les son los pros y contras de seguir este m茅todo?
-## [Examen posterior a la lecci贸n](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/18?loc=es)
+## [Examen posterior a la lecci贸n](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/18?loc=es)
## Revisi贸n y autoestudio
diff --git a/3-Web-App/1-Web-App/translations/README.it.md b/3-Web-App/1-Web-App/translations/README.it.md
index fec92dec..a2c9c7a8 100644
--- a/3-Web-App/1-Web-App/translations/README.it.md
+++ b/3-Web-App/1-Web-App/translations/README.it.md
@@ -11,7 +11,7 @@ Si continuer脿 a utilizzare il notebook per pulire i dati e addestrare il modell
Per fare ci貌, 猫 necessario creare un'app Web utilizzando Flask.
-## [Quiz pre-lezione](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/17/?loc=it)
+## [Quiz pre-lezione](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/17/?loc=it)
## Costruire un'app
@@ -334,7 +334,7 @@ In un ambiente professionale, si pu貌 vedere quanto sia necessaria una buona com
Invece di lavorare su un notebook e importare il modello nell'app Flask, si pu貌 addestrare il modello direttamente nell'app Flask! Provare a convertire il codice Python nel notebook, magari dopo che i dati sono stati puliti, per addestrare il modello dall'interno dell'app su un percorso chiamato `/train`. Quali sono i pro e i contro nel seguire questo metodo?
-## [Quiz post-lezione](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/18/?loc=it)
+## [Quiz post-lezione](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/18/?loc=it)
## Revisione e Auto Apprendimento
diff --git a/3-Web-App/1-Web-App/translations/README.ja.md b/3-Web-App/1-Web-App/translations/README.ja.md
index 7d18f6bf..f5355dce 100644
--- a/3-Web-App/1-Web-App/translations/README.ja.md
+++ b/3-Web-App/1-Web-App/translations/README.ja.md
@@ -11,7 +11,7 @@
銇濄伄銇熴倎銇伅銆丗lask銈掍娇銇ｃ仸Web銈€儣銉倰妲嬬瘔銇欍倠蹇呰銇屻亗銈娿伨銇欍€�
-## [璎涚京鍓嶃伄灏忋儐銈广儓](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/17?loc=ja)
+## [璎涚京鍓嶃伄灏忋儐銈广儓](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/17?loc=ja)
## 銈€儣銉伄妲嬬瘔
@@ -334,7 +334,7 @@ print(model.predict([[50,44,-12]]))
銉庛兗銉堛儢銉冦偗銇т綔妤仐銇︺儮銉囥儷銈扚lask銈€儣銉伀銈ゃ兂銉濄兗銉堛仚銈嬩唬銈忋倞銇€丗lask銈€儣銉伄涓仹銉€儑銉倰銉堛儸銉笺儖銉炽偘銇欍倠銇撱仺銇屻仹銇嶃伨銇欍€傘亰銇濄倝銇忋儑銉笺偪銈掋偗銉兗銉嬨兂銈般仐銇熷緦銇仾銈娿伨銇欍亴銆併儙銉笺儓銉栥儍銈唴銇甈ython銈炽兗銉夈倰澶夋彌銇椼仸銆併偄銉椼儶鍐呫伄 `train` 銇ㄣ亜銇嗐儜銈广仹銉€儑銉倰瀛︾繏銇椼仸銇裤仸銇忋仩銇曘亜銆傘亾銇柟娉曘倰鎺＄敤銇欍倠銇撱仺銇暦鎵€銇ㄧ煭鎵€銇綍銇с仐銈囥亞銇嬶紵
-## [璎涚京寰屻伄灏忋儐銈广儓](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/18?loc=ja)
+## [璎涚京寰屻伄灏忋儐銈广儓](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/18?loc=ja)
## 鎸倞杩斻倞銇ㄨ嚜涓诲缈�
diff --git a/3-Web-App/1-Web-App/translations/README.ko.md b/3-Web-App/1-Web-App/translations/README.ko.md
index a25e5be6..02f5fc54 100644
--- a/3-Web-App/1-Web-App/translations/README.ko.md
+++ b/3-Web-App/1-Web-App/translations/README.ko.md
@@ -11,7 +11,7 @@
鞚措煬氅�, Flask搿� 鞗� 鞎膘潉 毵岆摛鞏挫暭 頃╇媹雼�.
-## [臧曥潣 鞝� 韤挫](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/17/)
+## [臧曥潣 鞝� 韤挫](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/17/)
## 鞎� 毵岆摛旮�
@@ -335,7 +335,7 @@ Flask鞕€ pickled 氇嵏瓿� 臧欖澊, 氇嵏鞚� 靷毄頃橂姅 鞚� 氚╈嫕鞚€, 牍勱祼
雲疙姼攵侅棎靹� 鞛戩劚頃橁碃 Flask 鞎膘棎靹� 氇嵏鞚� 臧€鞝胳槫電� 雽€鞁�, Flask 鞎膘棎靹� 氚旊 氇嵏鞚� 頉堧牗頃� 靾� 鞛堨姷雼堧嫟! 鞏挫⿲氅� 雿办澊韯半ゼ 鞝曤Μ頃橁碃, 雲疙姼攵侅棎靹� Python 旖旊摐搿� 氤€頇橅暣靹�, `train`鞚措澕瓿� 攵堧Μ電� 霛检毎韯半 鞎膘棎靹� 氇嵏鞚� 頉堧牗頃╇媹雼�. 鞚措煬頃� 氚╈嫕鞚� 於旉惮頄堨潉 霑� 鞛レ爯瓿� 雼爯鞚€ 氍挫棁鞚戈皜鞖�?
-## [臧曥潣 頉� 韤挫](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/18/)
+## [臧曥潣 頉� 韤挫](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/18/)
## 瓴€韱� & 鞛愱赴欤茧弰 頃欖姷
diff --git a/3-Web-App/1-Web-App/translations/README.pt-br.md b/3-Web-App/1-Web-App/translations/README.pt-br.md
index 7fdf3a7f..22bd111b 100644
--- a/3-Web-App/1-Web-App/translations/README.pt-br.md
+++ b/3-Web-App/1-Web-App/translations/README.pt-br.md
@@ -11,7 +11,7 @@ Continuaremos nosso uso de notebooks para limpar dados e treinar nosso modelo, m
Para fazer isso, voc锚 precisa construir um aplicativo da web usando o Flask.
-## [Teste pr茅-aula](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/17?loc=ptbr)
+## [Teste pr茅-aula](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/17?loc=ptbr)
## Construindo um aplicativo
@@ -337,7 +337,7 @@ Em um ambiente profissional, voc锚 pode ver como uma boa comunica莽茫o 茅 necess
Em vez de trabalhar em um notebook e importar o modelo para o aplicativo Flask, voc锚 pode treinar o modelo diretamente no aplicativo Flask! Tente converter seu c贸digo Python no notebook, talvez depois que seus dados forem limpos, para treinar o modelo de dentro do aplicativo em uma rota chamada `train`. Quais s茫o os pr贸s e os contras de seguir esse m茅todo?
-## [Teste p贸s-aula](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/18?loc=ptbr)
+## [Teste p贸s-aula](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/18?loc=ptbr)
## Revis茫o e autoestudo
diff --git a/3-Web-App/1-Web-App/translations/README.pt.md b/3-Web-App/1-Web-App/translations/README.pt.md
index 92c02107..070065d3 100644
--- a/3-Web-App/1-Web-App/translations/README.pt.md
+++ b/3-Web-App/1-Web-App/translations/README.pt.md
@@ -11,7 +11,7 @@ Continuaremos a usar notebooks para limpar dados e treinar nosso modelo, mas voc
Para fazer isso, voc锚 precisa construir um aplicativo Web usando Flask.
-## [Teste de pr茅-aula](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/17/)
+## [Teste de pr茅-aula](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/17/)
## Criando um aplicativo
@@ -336,7 +336,7 @@ Em um ambiente profissional, voc锚 pode ver como uma boa comunica莽茫o 茅 necess
Em vez de trabalhar em um notebook e importar o modelo para o aplicativo Flask, voc锚 poderia treinar o modelo dentro do aplicativo Flask! Tente converter seu c贸digo Python no notebook, talvez depois que seus dados forem limpos, para treinar o modelo de dentro do aplicativo em uma rota chamada `train`. Quais s茫o os pr贸s e contras de se buscar esse m茅todo?
-## [Teste p贸s-aula](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/18/)
+## [Teste p贸s-aula](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/18/)
## An谩lise e autoestudo
diff --git a/3-Web-App/1-Web-App/translations/README.zh-cn.md b/3-Web-App/1-Web-App/translations/README.zh-cn.md
index 8e4741ba..44cc797e 100644
--- a/3-Web-App/1-Web-App/translations/README.zh-cn.md
+++ b/3-Web-App/1-Web-App/translations/README.zh-cn.md
@@ -11,7 +11,7 @@
涓烘锛屼綘闇€瑕佷娇鐢� Flask 鏋勫缓涓€涓� web 搴旂敤绋嬪簭銆�
-## [璇惧墠娴媇(https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/17/)
+## [璇惧墠娴媇(https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/17/)
## 鏋勫缓搴旂敤绋嬪簭
@@ -334,7 +334,7 @@ print(model.predict([[50,44,-12]]))
浣犲彲浠ュ湪 Flask 搴旂敤绋嬪簭涓缁冩ā鍨嬶紝鑰屼笉鏄湪 notebook 涓婂伐浣滃苟灏嗘ā鍨嬪鍏� Flask 搴旂敤绋嬪簭锛佸皾璇曞湪 notebook 涓浆鎹� Python 浠ｇ爜锛屽彲鑳芥槸鍦ㄦ竻闄ゆ暟鎹箣鍚庯紝浠庡簲鐢ㄧ▼搴忎腑鐨勪竴涓悕涓� `train` 鐨勮矾寰勮缁冩ā鍨嬨€傞噰鐢ㄨ繖绉嶆柟娉曠殑鍒╁紛鏄粈涔堬紵
-## [璇惧悗娴媇(https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/18/)
+## [璇惧悗娴媇(https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/18/)
## 澶嶄範涓庤嚜瀛�
diff --git a/4-Classification/1-Introduction/README.md b/4-Classification/1-Introduction/README.md
index 1fd35e8c..513fd09b 100644
--- a/4-Classification/1-Introduction/README.md
+++ b/4-Classification/1-Introduction/README.md
@@ -19,7 +19,7 @@ Remember:
Classification uses various algorithms to determine other ways of determining a data point's label or class. Let's work with this cuisine data to see whether, by observing a group of ingredients, we can determine its cuisine of origin.
-## [Pre-lecture quiz](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/19/)
+## [Pre-lecture quiz](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/19/)
> ### [This lesson is available in R!](./solution/R/lesson_10-R.ipynb)
@@ -288,7 +288,7 @@ Now that you have cleaned the data, use [SMOTE](https://imbalanced-learn.org/dev
This curriculum contains several interesting datasets. Dig through the `data` folders and see if any contain datasets that would be appropriate for binary or multi-class classification? What questions would you ask of this dataset?
-## [Post-lecture quiz](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/20/)
+## [Post-lecture quiz](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/20/)
## Review & Self Study
diff --git a/4-Classification/1-Introduction/solution/R/lesson_10-R.ipynb b/4-Classification/1-Introduction/solution/R/lesson_10-R.ipynb
index 9fb94e30..83babad7 100644
--- a/4-Classification/1-Introduction/solution/R/lesson_10-R.ipynb
+++ b/4-Classification/1-Introduction/solution/R/lesson_10-R.ipynb
@@ -50,7 +50,7 @@
"\n",
"Classification uses various algorithms to determine other ways of determining a data point's label or class. Let's work with this cuisine data to see whether, by observing a group of ingredients, we can determine its cuisine of origin.\n",
"\n",
- "### [**Pre-lecture quiz**](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/19/)\n",
+ "### [**Pre-lecture quiz**](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/19/)\n",
"\n",
"### **Introduction**\n",
"\n",
@@ -692,7 +692,7 @@
"\r\n",
"This curriculum contains several interesting datasets. Dig through the `data` folders and see if any contain datasets that would be appropriate for binary or multi-class classification? What questions would you ask of this dataset?\r\n",
"\r\n",
- "## [**Post-lecture quiz**](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/20/)\r\n",
+ "## [**Post-lecture quiz**](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/20/)\r\n",
"\r\n",
"## **Review & Self Study**\r\n",
"\r\n",
diff --git a/4-Classification/1-Introduction/solution/R/lesson_10.Rmd b/4-Classification/1-Introduction/solution/R/lesson_10.Rmd
index 06959b65..dc81b816 100644
--- a/4-Classification/1-Introduction/solution/R/lesson_10.Rmd
+++ b/4-Classification/1-Introduction/solution/R/lesson_10.Rmd
@@ -26,7 +26,7 @@ Remember:
Classification uses various algorithms to determine other ways of determining a data point's label or class. Let's work with this cuisine data to see whether, by observing a group of ingredients, we can determine its cuisine of origin.
-### [**Pre-lecture quiz**](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/19/)
+### [**Pre-lecture quiz**](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/19/)
### **Introduction**
@@ -403,7 +403,7 @@ This fresh CSV can now be found in the root data folder.
This curriculum contains several interesting datasets. Dig through the `data` folders and see if any contain datasets that would be appropriate for binary or multi-class classification? What questions would you ask of this dataset?
-## [**Post-lecture quiz**](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/20/)
+## [**Post-lecture quiz**](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/20/)
## **Review & Self Study**
diff --git a/4-Classification/1-Introduction/translations/README.es.md b/4-Classification/1-Introduction/translations/README.es.md
index b1ee4742..26fc5991 100644
--- a/4-Classification/1-Introduction/translations/README.es.md
+++ b/4-Classification/1-Introduction/translations/README.es.md
@@ -19,7 +19,7 @@ Recuerda:
La clasificaci贸n utiliza varios algor铆tmos para determinar otras formas de determinar la clase o etiqueta de un punto de datos. Trabajemos con estos datos de cocina para ver si, al observar un grupo de ingredientes, podemos determinar su cocina u origen.
-## [Examen previo a la lecci贸n](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/19?loc=es)
+## [Examen previo a la lecci贸n](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/19?loc=es)
> ### [隆Esta lecci贸n est谩 disponible en R!](./solution/R/lesson_10-R.ipynb)
@@ -288,7 +288,7 @@ Ahora que has limpiado los datos, usa [SMOTE](https://imbalanced-learn.org/dev/r
Este plan de estudios contiene varios conjuntos de datos interesantes. Profundiza en los directorios `data` y ve si alguno contiene conjuntos de datos que ser铆an apropiados para clasificaci贸n binaria o multiclase. 驴Qu茅 preguntas har铆as a este conunto de datos?
-## [Examen posterior a la lecci贸n](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/20?loc=es)
+## [Examen posterior a la lecci贸n](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/20?loc=es)
## Revisi贸n y autoestudio
diff --git a/4-Classification/1-Introduction/translations/README.it.md b/4-Classification/1-Introduction/translations/README.it.md
index 5bfd7fb0..eb7df4c5 100644
--- a/4-Classification/1-Introduction/translations/README.it.md
+++ b/4-Classification/1-Introduction/translations/README.it.md
@@ -19,7 +19,7 @@ Ricordare:
La classificazione utilizza vari algoritmi per determinare altri modi per definire l'etichetta o la classe di un punto dati. Si lavorer脿 con questi dati di cucina per vedere se, osservando un gruppo di ingredienti, 猫 possibile determinarne la cucina di origine.
-## [Quiz pre-lezione](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/19/?loc=it)
+## [Quiz pre-lezione](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/19/?loc=it)
### Introduzione
@@ -286,7 +286,7 @@ Ora che i dati sono puliti, si usa [SMOTE](https://imbalanced-learn.org/dev/refe
Questo programma di studi contiene diversi insiemi di dati interessanti. Esaminare le cartelle `data` e vedere se contiene insiemi di dati che sarebbero appropriati per la classificazione binaria o multiclasse. Quali domande si farebbero a questo insieme di dati?
-## [Quiz post-lezione](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/20/?loc=it)
+## [Quiz post-lezione](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/20/?loc=it)
## Revisione e Auto Apprendimento
diff --git a/4-Classification/1-Introduction/translations/README.ko.md b/4-Classification/1-Introduction/translations/README.ko.md
index abe82d3d..72dcc04b 100644
--- a/4-Classification/1-Introduction/translations/README.ko.md
+++ b/4-Classification/1-Introduction/translations/README.ko.md
@@ -19,7 +19,7 @@ Classification鞚€ regression 旮办垹瓿� 瓿淀喌鞝愳澊 毵庫潃 [supervised learning]
Classification鞚€ 雼れ枒頃� 鞎岅碃毽鞙茧 雿办澊韯� 韽澑韸胳潣 霛茧波 順轨潃 韥措灅鞀るゼ 瓴办爼頃� 雼るジ 氚╈嫕鞚� 瓿犽雼堧嫟. 鞖旊Μ 雿办澊韯半, 鞛 攴鸽９鞚� 彀眷晞靹�, 鞝勴喌 鞖旊Μ搿� 瓴办爼頃� 靾� 鞛堧姅歆€ 鞎岇晞氤措牑 頃╇媹雼�.
-## [臧曥潣 鞝� 韤挫](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/19/)
+## [臧曥潣 鞝� 韤挫](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/19/)
### 靻岅皽
@@ -287,7 +287,7 @@ Scikit-learn鞚€ 頃搓舶頃橁碃 鞁鹅潃 氍胳牅鞚� 韮€鞛呾棎 霐半澕靹�, 雿办澊韯半ゼ
頃措嫻 旎るΜ韥橂熂鞚€ 鞐煬 頋ル搿滌毚 雿办澊韯办厠鞚� 韽暔頃橁碃 鞛堨姷雼堧嫟. `data` 韽措崝毳� 韺岆炒氅挫劀 binary 霕愲姅 multi-class classification鞐� 鞝侂嫻頃� 雿办澊韯办厠鞚� 韽暔霅橃柎 鞛堧倶鞖�? 雿办澊韯办厠鞐� 鞏措柣瓴� 氍检柎氤措倶鞖�?
-## [臧曥潣 頉� 韤挫](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/20/)
+## [臧曥潣 頉� 韤挫](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/20/)
## 瓴€韱� & 鞛愱赴欤茧弰 頃欖姷
diff --git a/4-Classification/1-Introduction/translations/README.pt-br.md b/4-Classification/1-Introduction/translations/README.pt-br.md
index 68dd89cf..fb16c78f 100644
--- a/4-Classification/1-Introduction/translations/README.pt-br.md
+++ b/4-Classification/1-Introduction/translations/README.pt-br.md
@@ -19,7 +19,7 @@ Lembre-se:
A classifica莽茫o usa v谩rios algoritmos para determinar outras maneiras de determinar o r贸tulo ou a classe de um ponto de dados ou objeto. Vamos trabalhar com dados sobre culin谩ria para ver se, observando um grupo de ingredientes, podemos determinar sua culin谩ria de origem.
-## [Question谩rio inicial](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/19/?loc=ptbr)
+## [Question谩rio inicial](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/19/?loc=ptbr)
> ### [Esta li莽茫o est谩 dispon铆vel em R!](../solution/R/lesson_10-R.ipynb)
@@ -288,7 +288,7 @@ Agora que voc锚 limpou os dados, use a [SMOTE](https://imbalanced-learn.org/dev/
Esta li莽茫o cont茅m v谩rios _datasets_ interessantes. Explore os arquivos da pasta `data` e veja quais _datasets_ seriam apropriados para classifica莽茫o bin谩ria ou multiclasse. Quais perguntas voc锚 faria sobre estes _datasets_?
-## [Question谩rio para fixa莽茫o](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/20?loc=ptbr)
+## [Question谩rio para fixa莽茫o](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/20?loc=ptbr)
## Revis茫o e Auto Aprendizagem
diff --git a/4-Classification/1-Introduction/translations/README.tr.md b/4-Classification/1-Introduction/translations/README.tr.md
index 876c1ce7..84f979f2 100644
--- a/4-Classification/1-Introduction/translations/README.tr.md
+++ b/4-Classification/1-Introduction/translations/README.tr.md
@@ -19,7 +19,7 @@ Hat谋rlay谋n:
S谋n谋fland谋rma, bir veri noktas谋n谋n etiketini veya s谋n谋f谋n谋 belirlemek i莽in farkl谋 yollar belirlemek 眉zere 莽e艧itli algoritmalar kullan谋r. Bir grup malzemeyi g枚zlemleyerek k枚keninin hangi mutfak oldu臒unu belirleyip belirleyemeyece臒imizi g枚rmek i莽in bu mutfak verisiyle 莽al谋艧al谋m.
-## [Ders 枚ncesi k谋sa s谋nav谋](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/19/?loc=tr)
+## [Ders 枚ncesi k谋sa s谋nav谋](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/19/?loc=tr)
### Giri艧
@@ -287,7 +287,7 @@ Veriyi temizlediniz, 艧imdi [SMOTE](https://imbalanced-learn.org/dev/references/
Bu 枚臒retim program谋 farkl谋 ilgi 莽ekici veri setleri i莽ermekte. `data` klas枚rlerini inceleyin ve ikili veya 莽ok s谋n谋fl谋 s谋n谋fland谋rma i莽in uygun olabilecek veri setleri bulunduran var m谋, bak谋n. Bu veri seti i莽in hangi sorular谋 sorabilirdiniz?
-## [Ders sonras谋 k谋sa s谋nav谋](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/20/?loc=tr)
+## [Ders sonras谋 k谋sa s谋nav谋](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/20/?loc=tr)
## G枚zden Ge莽irme & Kendi Kendine 脟al谋艧ma
diff --git a/4-Classification/1-Introduction/translations/README.zh-cn.md b/4-Classification/1-Introduction/translations/README.zh-cn.md
index 70954b6f..9c762e84 100644
--- a/4-Classification/1-Introduction/translations/README.zh-cn.md
+++ b/4-Classification/1-Introduction/translations/README.zh-cn.md
@@ -19,7 +19,7 @@
鍒嗙被鏂规硶閲囩敤澶氱绠楁硶鏉ョ‘瀹氬叾浠栧彲浠ョ敤鏉ョ‘瀹氫竴涓暟鎹偣鐨勬爣绛炬垨绫诲埆鐨勬柟娉曘€傝鎴戜滑鏉ョ爺绌朵竴涓嬭繖涓暟鎹泦锛岀湅鐪嬫垜浠兘鍚﹂€氳繃瑙傚療鑿滆偞鐨勫師鏂欐潵纭畾瀹冪殑婧愬ご銆�
-## [璇剧▼鍓嶇殑灏忛棶棰榏(https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/19/)
+## [璇剧▼鍓嶇殑灏忛棶棰榏(https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/19/)
鍒嗙被鏄満鍣ㄥ涔犵爺绌惰€呭拰鏁版嵁绉戝瀹朵娇鐢ㄧ殑涓€绉嶅熀鏈柟娉曘€備粠鍩烘湰鐨勪簩鍏冨垎绫伙紙杩欐槸涓嶆槸涓€浠藉瀮鍦鹃偖浠讹紵锛夊埌澶嶆潅鐨勫浘鐗囧垎绫诲拰浣跨敤璁＄畻鏈鸿瑙夌殑鍒嗗壊鎶€鏈紝瀹冮兘鏄皢鏁版嵁鍒嗙被骞舵彁鍑虹浉鍏抽棶棰樼殑鏈夋晥宸ュ叿銆�
@@ -280,7 +280,7 @@ Scikit-learn 椤圭洰鎻愪緵澶氱瀵规暟鎹繘琛屽垎绫荤殑绠楁硶锛屼綘闇€瑕佹牴鎹�
鏈」鐩殑鍏ㄩ儴璇剧▼鍚湁寰堝鏈夎叮鐨勬暟鎹泦銆� 鎺㈢储涓€涓� `data` 鏂囦欢澶癸紝鐪嬬湅杩欓噷闈㈡湁娌℃湁閫傚悎浜屽厓鍒嗙被銆佸鍏冨垎绫荤畻娉曠殑鏁版嵁闆嗭紝鍐嶆兂涓€涓嬩綘瀵硅繖浜涙暟鎹泦鏈夋病鏈変粈涔堟兂闂殑闂銆�
-## [璇惧悗缁冧範](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/20/)
+## [璇惧悗缁冧範](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/20/)
## 鍥為【 & 鑷
diff --git a/4-Classification/2-Classifiers-1/README.md b/4-Classification/2-Classifiers-1/README.md
index 7555000c..434c4893 100644
--- a/4-Classification/2-Classifiers-1/README.md
+++ b/4-Classification/2-Classifiers-1/README.md
@@ -4,7 +4,7 @@ In this lesson, you will use the dataset you saved from the last lesson full of
You will use this dataset with a variety of classifiers to _predict a given national cuisine based on a group of ingredients_. While doing so, you'll learn more about some of the ways that algorithms can be leveraged for classification tasks.
-## [Pre-lecture quiz](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/21/)
+## [Pre-lecture quiz](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/21/)
# Preparation
Assuming you completed [Lesson 1](../1-Introduction/README.md), make sure that a _cleaned_cuisines.csv_ file exists in the root `/data` folder for these four lessons.
@@ -231,7 +231,7 @@ Since you are using the multiclass case, you need to choose what _scheme_ to use
In this lesson, you used your cleaned data to build a machine learning model that can predict a national cuisine based on a series of ingredients. Take some time to read through the many options Scikit-learn provides to classify data. Dig deeper into the concept of 'solver' to understand what goes on behind the scenes.
-## [Post-lecture quiz](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/22/)
+## [Post-lecture quiz](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/22/)
## Review & Self Study
diff --git a/4-Classification/2-Classifiers-1/solution/R/lesson_11-R.ipynb b/4-Classification/2-Classifiers-1/solution/R/lesson_11-R.ipynb
index 63d42391..8376311e 100644
--- a/4-Classification/2-Classifiers-1/solution/R/lesson_11-R.ipynb
+++ b/4-Classification/2-Classifiers-1/solution/R/lesson_11-R.ipynb
@@ -33,7 +33,7 @@
"\n",
"In this lesson, we'll explore a variety of classifiers to *predict a given national cuisine based on a group of ingredients.* While doing so, we'll learn more about some of the ways that algorithms can be leveraged for classification tasks.\n",
"\n",
- "### [**Pre-lecture quiz**](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/21/)\n",
+ "### [**Pre-lecture quiz**](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/21/)\n",
"\n",
"### **Preparation**\n",
"\n",
diff --git a/4-Classification/2-Classifiers-1/solution/R/lesson_11.Rmd b/4-Classification/2-Classifiers-1/solution/R/lesson_11.Rmd
index 000142ec..8ba2fa36 100644
--- a/4-Classification/2-Classifiers-1/solution/R/lesson_11.Rmd
+++ b/4-Classification/2-Classifiers-1/solution/R/lesson_11.Rmd
@@ -14,7 +14,7 @@ output:
In this lesson, we'll explore a variety of classifiers to *predict a given national cuisine based on a group of ingredients.* While doing so, we'll learn more about some of the ways that algorithms can be leveraged for classification tasks.
-### [**Pre-lecture quiz**](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/21/)
+### [**Pre-lecture quiz**](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/21/)
### **Preparation**
diff --git a/4-Classification/2-Classifiers-1/translations/README.es.md b/4-Classification/2-Classifiers-1/translations/README.es.md
index 1568d172..6e8a799e 100644
--- a/4-Classification/2-Classifiers-1/translations/README.es.md
+++ b/4-Classification/2-Classifiers-1/translations/README.es.md
@@ -4,7 +4,7 @@ En esta lecci贸n, usar谩s el conjunto de datos que guardaste en la 煤ltima lecci
Usar谩s este conjunto de datos con una variedad de clasificadores para _predecir una cocina nacional dada basado en un grupo de ingredientes_. Mientras lo haces, aprender谩s m谩s acerca de algunas formas en que los algoritmos pueden ser aprovechados para las tareas de clasificaci贸n.
-## [Examen previo a la lecci贸n](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/21?loc=es)
+## [Examen previo a la lecci贸n](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/21?loc=es)
# Preparaci贸n
@@ -234,7 +234,7 @@ Ya que est谩s usando un caso multiclase, necesitas elegir qu茅 _esquema_ usar y
En esta lecci贸n, usaste tus datos limpios para construir un modelo de aprendizaje autom谩tico que puede predecir una cocina nacional basado en una serie de ingredientes. Toma un tiempo para leer las diversas opciones que provee Scikit-learn para clasificar los datos. Profundiza en el concepto de 'solucionador' para comprender que sucede detr谩s de escena.
-## [Examen posterior a la lecci贸n](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/22?loc=es)
+## [Examen posterior a la lecci贸n](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/22?loc=es)
## Revisi贸n y autoestudio
diff --git a/4-Classification/2-Classifiers-1/translations/README.it.md b/4-Classification/2-Classifiers-1/translations/README.it.md
index ed85e784..d02d767c 100644
--- a/4-Classification/2-Classifiers-1/translations/README.it.md
+++ b/4-Classification/2-Classifiers-1/translations/README.it.md
@@ -4,7 +4,7 @@ In questa lezione, si utilizzer脿 l'insieme di dati salvati dall'ultima lezione,
Si utilizzer脿 questo insieme di dati con una variet脿 di classificatori per _prevedere una determinata cucina nazionale in base a un gruppo di ingredienti_. Mentre si fa questo, si imparer脿 di pi霉 su alcuni dei modi in cui gli algoritmi possono essere sfruttati per le attivit脿 di classificazione.
-## [Quiz pre-lezione](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/21/?loc=it)
+## [Quiz pre-lezione](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/21/?loc=it)
# Preparazione
Supponendo che la [Lezione 1](../1-Introduction/README.md) sia stata completata, assicurarsi che _esista_ un file clean_cuisines.csv nella cartella in radice `/data` per queste quattro lezioni.
@@ -232,7 +232,7 @@ Poich茅 si sta utilizzando il caso multiclasse, si deve scegliere quale _schema_
In questa lezione, sono stati utilizzati dati puliti per creare un modello di apprendimento automatico in grado di prevedere una cucina nazionale basata su una serie di ingredienti. Si prenda del tempo per leggere le numerose opzioni fornite da Scikit-learn per classificare i dati. Approfondire il concetto di "risolutore" per capire cosa succede dietro le quinte.
-## [Quiz post-lezione](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/22/?loc=it)
+## [Quiz post-lezione](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/22/?loc=it)
## Revisione e Auto Apprendimento
Approfondire un po' la matematica alla base della regressione logistica in [questa lezione](https://people.eecs.berkeley.edu/~russell/classes/cs194/f11/lectures/CS194%20Fall%202011%20Lecture%2006.pdf)
diff --git a/4-Classification/2-Classifiers-1/translations/README.ko.md b/4-Classification/2-Classifiers-1/translations/README.ko.md
index d6db0d07..bbebca7e 100644
--- a/4-Classification/2-Classifiers-1/translations/README.ko.md
+++ b/4-Classification/2-Classifiers-1/translations/README.ko.md
@@ -4,7 +4,7 @@
雼れ枒頃� classifiers鞕€ 雿办澊韯办厠鞚� 靷毄頃挫劀 _鞛 攴鸽９ 旮半皹鞙茧 欤检柎歆� 甑 鞖旊Μ毳� 鞓堨浮_ 頃╇媹雼�. 鞚措煬電� 霃欖晥, classification 鞛戩梾鞐� 鞎岅碃毽鞚� 頇滌毄頃� 氇� 氚╈嫕鞐� 雽€頃� 鞛愳劯頌� 氚办泴氤� 鞓堨爼鞛呺媹雼�.
-## [臧曥潣 鞝� 韤挫](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/21/)
+## [臧曥潣 鞝� 韤挫](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/21/)
## 欷€牍勴晿旮�
@@ -233,7 +233,7 @@ multiclass 旒€鞚挫姢搿�, 靷毄頃� _scheme_ 鞕€ 靹れ爼頃� _solver_ 毳� 靹犿儩頃�
鞚� 臧曥潣鞐愳劀, 鞝曤Μ霅� 雿办澊韯半 鞛鞚� 鞁滊Μ歃堧ゼ 旮半皹鞙茧 甑 鞖旊Μ毳� 鞓堨浮頃� 靾� 鞛堧姅 毹胳嫚霟嫕 氇嵏鞚� 毵岆摛鞐堨姷雼堧嫟. 鞁滉皠鞚� 韴瀽頃挫劀 Scikit-learn鞚� 雿办澊韯半ゼ 攵勲頃橁赴 鞙勴暣 鞝滉车頃橂姅 雼れ枒頃� 鞓奠厴鞚� 鞚届柎氪呺媹雼�. 氍措寑 霋れ棎靹� 靸濌赴電� 鞚检潉 鞚错暣頃橁赴 鞙勴暣靹� 'solver'鞚� 臧滊厫鞚� 旯婈矊 韺岆磪雼堧嫟.
-## [臧曥潣 頉� 韤挫](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/22/)
+## [臧曥潣 頉� 韤挫](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/22/)
## 瓴€韱� & 鞛愱赴欤茧弰 頃欖姷
[this lesson](https://people.eecs.berkeley.edu/~russell/classes/cs194/f11/lectures/CS194%20Fall%202011%20Lecture%2006.pdf)鞐愳劀 logistic regression 霋れ潣 靾橅暀鞐� 雽€頃挫劀 雿� 鞛愳劯頌� 韺岆磪雼堧嫟.
diff --git a/4-Classification/2-Classifiers-1/translations/README.pt-br.md b/4-Classification/2-Classifiers-1/translations/README.pt-br.md
index 193f8d85..afbf15c7 100644
--- a/4-Classification/2-Classifiers-1/translations/README.pt-br.md
+++ b/4-Classification/2-Classifiers-1/translations/README.pt-br.md
@@ -4,7 +4,7 @@ Nesta li莽茫o, voc锚 usar谩 o _dataset_ balanceado e tratado que salvou da 煤lti
Voc锚 usar谩 este _dataset_ com uma variedade de classificadores para _prever uma determinada culin谩ria nacional com base em um grupo de ingredientes_. Enquanto isso, voc锚 aprender谩 mais sobre algumas das maneiras como os algoritmos podem ser aproveitados para tarefas de classifica莽茫o.
-## [Question谩rio inicial](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/21?loc=ptbr)
+## [Question谩rio inicial](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/21?loc=ptbr)
# Prepara莽茫o
@@ -232,7 +232,7 @@ J谩 que estamos usando um caso multiclasse, voc锚 precisa escolher qual _scheme_
Nesta li莽茫o, voc锚 usou seus dados para construir um modelo de aprendizado de m谩quina que pode prever uma culin谩ria nacional com base em uma s茅rie de ingredientes. Reserve algum tempo para ler as op莽玫es que o Scikit-learn oferece para classificar dados. Aprofunde-se no conceito de 'solucionador' para entender o que acontece nos bastidores.
-## [Question谩rio para fixa莽茫o](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/22?loc=ptbr)
+## [Question谩rio para fixa莽茫o](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/22?loc=ptbr)
## Revis茫o e Auto Aprendizagem
diff --git a/4-Classification/2-Classifiers-1/translations/README.tr.md b/4-Classification/2-Classifiers-1/translations/README.tr.md
index f59a39a8..a4c42132 100644
--- a/4-Classification/2-Classifiers-1/translations/README.tr.md
+++ b/4-Classification/2-Classifiers-1/translations/README.tr.md
@@ -4,7 +4,7 @@ Bu derste, mutfaklarla ilgili dengeli ve temiz veriyle dolu, ge莽en dersten kayd
Bu veri setini 莽e艧itli s谋n谋fland谋r谋c谋larla _bir grup malzemeyi baz alarak verilen bir ulusal mutfa臒谋 枚ng枚rmek_ i莽in kullanacaks谋n谋z. Bunu yaparken, s谋n谋fland谋rma g枚revleri i莽in algoritmalar谋n leveraj edilebilece臒i yollardan baz谋lar谋 hakk谋nda daha fazla bilgi edineceksiniz.
-## [Ders 枚ncesi k谋sa s谋nav谋](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/21/?loc=tr)
+## [Ders 枚ncesi k谋sa s谋nav谋](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/21/?loc=tr)
# Haz谋rl谋k
[Birinci dersi](../../1-Introduction/README.md) tamamlad谋臒谋n谋z谋 varsay谋yoruz, dolay谋s谋yla bu d枚rt ders i莽in _cleaned_cuisines.csv_ dosyas谋n谋n k枚k `/data` klas枚r眉nde var oldu臒undan emin olun.
@@ -231,7 +231,7 @@ X_train, X_test, y_train, y_test = train_test_split(cuisines_feature_df, cuisine
Bu derste, bir grup malzemeyi baz alarak bir ulusal mutfa臒谋 枚ng枚rebilen bir makine 枚臒renimi modeli olu艧turmak i莽in temiz verinizi kulland谋n谋z. Scikit-learn'眉n veri s谋n谋fland谋rmak i莽in sa臒lad谋臒谋 bir莽ok y枚ntemi okumak i莽in biraz vakit ay谋r谋n. Arka tarafta neler oldu臒unu anlamak i莽in '莽枚z眉c眉' kavram谋n谋 derinlemesine inceleyin.
-## [Ders sonras谋 k谋sa s谋nav谋](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/22/?loc=tr)
+## [Ders sonras谋 k谋sa s谋nav谋](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/22/?loc=tr)
## G枚zden ge莽irme & kendi kendine 莽al谋艧ma
diff --git a/4-Classification/2-Classifiers-1/translations/README.zh-cn.md b/4-Classification/2-Classifiers-1/translations/README.zh-cn.md
index 06761a10..7f220b92 100644
--- a/4-Classification/2-Classifiers-1/translations/README.zh-cn.md
+++ b/4-Classification/2-Classifiers-1/translations/README.zh-cn.md
@@ -4,7 +4,7 @@
浣犲皢浣跨敤姝ゆ暟鎹泦鍜屽悇绉嶅垎绫诲櫒锛宊鏍规嵁涓€缁勯厤鏂欓娴嬭繖鏄摢涓€鍥藉鐨勭編椋焈銆傚湪姝よ繃绋嬩腑锛屼綘灏嗗鍒版洿澶氱敤鏉ユ潈琛″垎绫讳换鍔＄畻娉曠殑鏂规硶
-## [璇惧墠娴嬮獙](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/21/)
+## [璇惧墠娴嬮獙](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/21/)
# 鍑嗗宸ヤ綔
@@ -230,7 +230,7 @@ X_train, X_test, y_train, y_test = train_test_split(cuisines_feature_df, cuisine
鍦ㄦ湰璇剧▼涓紝浣犱娇鐢ㄤ簡娓呮礂鍚庣殑鏁版嵁寤虹珛浜嗕竴涓満鍣ㄥ涔犵殑妯″瀷锛岃繖涓ā鍨嬭兘澶熸牴鎹緭鍏ョ殑涓€绯诲垪鐨勯厤鏂欐潵棰勬祴鑿滃搧鏉ヨ嚜浜庡摢涓浗瀹躲€傝鍐嶈姳鐐规椂闂撮槄璇讳竴涓� Scikit-learn 鎵€鎻愪緵鐨勫叧浜庡彲浠ョ敤鏉ュ垎绫绘暟鎹殑鍏朵粬鏂规硶鐨勮祫鏂欍€傛澶栵紝浣犱篃鍙互娣卞叆鐮旂┒涓€涓嬧€渟olver鈥濈殑姒傚康骞跺皾璇曚竴涓嬬悊瑙ｅ叾鑳屽悗鐨勫師鐞嗐€�
-## [璇惧悗娴嬮獙](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/22/)
+## [璇惧悗娴嬮獙](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/22/)
## 鍥為【涓庤嚜瀛�
diff --git a/4-Classification/3-Classifiers-2/README.md b/4-Classification/3-Classifiers-2/README.md
index 014a4662..3e8a4137 100644
--- a/4-Classification/3-Classifiers-2/README.md
+++ b/4-Classification/3-Classifiers-2/README.md
@@ -2,7 +2,7 @@
In this second classification lesson, you will explore more ways to classify numeric data. You will also learn about the ramifications for choosing one classifier over the other.
-## [Pre-lecture quiz](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/23/)
+## [Pre-lecture quiz](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/23/)
### Prerequisite
@@ -224,7 +224,7 @@ This method of Machine Learning "combines the predictions of several base estima
Each of these techniques has a large number of parameters that you can tweak. Research each one's default parameters and think about what tweaking these parameters would mean for the model's quality.
-## [Post-lecture quiz](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/24/)
+## [Post-lecture quiz](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/24/)
## Review & Self Study
diff --git a/4-Classification/3-Classifiers-2/solution/R/lesson_12-R.ipynb b/4-Classification/3-Classifiers-2/solution/R/lesson_12-R.ipynb
index 4c22b93e..260a35b2 100644
--- a/4-Classification/3-Classifiers-2/solution/R/lesson_12-R.ipynb
+++ b/4-Classification/3-Classifiers-2/solution/R/lesson_12-R.ipynb
@@ -35,7 +35,7 @@
"\n",
"In this second classification lesson, we will explore `more ways` to classify categorical data. We will also learn about the ramifications for choosing one classifier over the other.\n",
"\n",
- "### [**Pre-lecture quiz**](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/23/)\n",
+ "### [**Pre-lecture quiz**](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/23/)\n",
"\n",
"### **Prerequisite**\n",
"\n",
@@ -619,7 +619,7 @@
"\n",
"> In practice, we usually *estimate* the *best values* for these by training many models on a `simulated data set` and measuring how well all these models perform. This process is called **tuning**.\n",
"\n",
- "### [**Post-lecture quiz**](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/24/)\n",
+ "### [**Post-lecture quiz**](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/24/)\n",
"\n",
"### **Review & Self Study**\n",
"\n",
diff --git a/4-Classification/3-Classifiers-2/solution/R/lesson_12.Rmd b/4-Classification/3-Classifiers-2/solution/R/lesson_12.Rmd
index 526b8503..112399a9 100644
--- a/4-Classification/3-Classifiers-2/solution/R/lesson_12.Rmd
+++ b/4-Classification/3-Classifiers-2/solution/R/lesson_12.Rmd
@@ -14,7 +14,7 @@ output:
In this second classification lesson, we will explore `more ways` to classify categorical data. We will also learn about the ramifications for choosing one classifier over the other.
-### [**Pre-lecture quiz**](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/23/)
+### [**Pre-lecture quiz**](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/23/)
### **Prerequisite**
@@ -433,7 +433,7 @@ To find out more about a particular model and its parameters, use: `help("model"
> In practice, we usually *estimate* the *best values* for these by training many models on a `simulated data set` and measuring how well all these models perform. This process is called **tuning**.
-### [**Post-lecture quiz**](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/24/)
+### [**Post-lecture quiz**](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/24/)
### **Review & Self Study**
diff --git a/4-Classification/3-Classifiers-2/translations/README.es.md b/4-Classification/3-Classifiers-2/translations/README.es.md
index bd16d23f..ba3a942d 100644
--- a/4-Classification/3-Classifiers-2/translations/README.es.md
+++ b/4-Classification/3-Classifiers-2/translations/README.es.md
@@ -2,7 +2,7 @@
En esta segunda lecci贸n de clasificaci贸n, explorar谩s m谩s formas de clasificar datos num茅ricos. Tambi茅n aprender谩s acerca de las ramificaciones para elegir un clasificador en lugar de otro.
-## [Examen previo a la lecci贸n](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/23?loc=es)
+## [Examen previo a la lecci贸n](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/23?loc=es)
### Prerrequisito
@@ -223,7 +223,7 @@ Este m茅todo de aprendizaje autom谩tico "combina las predicciones de varios esti
Cada una de estas t茅cnicas tiene un gran n煤mero de par谩metros que puedes modificar. Investiga los par谩metros predeterminados de cada uno y piensa en lo que significar铆a el ajuste de estos par谩metros para la calidad del modelo.
-## [Examen posterior a la lecci贸n](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/24?loc=es)
+## [Examen posterior a la lecci贸n](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/24?loc=es)
## Revisi贸n y autoestudio
diff --git a/4-Classification/3-Classifiers-2/translations/README.it.md b/4-Classification/3-Classifiers-2/translations/README.it.md
index 8f4fdd03..634ae550 100644
--- a/4-Classification/3-Classifiers-2/translations/README.it.md
+++ b/4-Classification/3-Classifiers-2/translations/README.it.md
@@ -2,7 +2,7 @@
In questa seconda lezione sulla classificazione, si esploreranno pi霉 modi per classificare i dati numerici. Si Impareranno anche le ramificazioni per la scelta di un classificatore rispetto all'altro.
-## [Quiz pre-lezione](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/23/?loc=it)
+## [Quiz pre-lezione](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/23/?loc=it)
### Prerequisito
@@ -224,7 +224,7 @@ Questo metodo di Machine Learning "combina le previsioni di diversi stimatori di
Ognuna di queste tecniche ha un gran numero di parametri che si possono modificare. Ricercare i parametri predefiniti di ciascuno e pensare a cosa significherebbe modificare questi parametri per la qualit脿 del modello.
-## [Quiz post-lezione](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/24/?loc=it)
+## [Quiz post-lezione](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/24/?loc=it)
## Revisione e Auto Apprendimento
diff --git a/4-Classification/3-Classifiers-2/translations/README.ko.md b/4-Classification/3-Classifiers-2/translations/README.ko.md
index 78b67268..13968b5f 100644
--- a/4-Classification/3-Classifiers-2/translations/README.ko.md
+++ b/4-Classification/3-Classifiers-2/translations/README.ko.md
@@ -2,7 +2,7 @@
霊愲矆歆� classification 臧曥潣鞐愳劀, 靾瀽 雿办澊韯半ゼ 攵勲頃橂姅 雿� 毵庫潃 氚╈嫕鞚� 鞎岇晞氪呺媹雼�. 雼るジ 瓴冸炒雼� 頃橂倶鞚� classifier毳� 靹犿儩頃橂姅 韺岅笁須臣霃� 氚办毎瓴� 霅╇媹雼�.
-## [臧曥潣 鞝� 韤挫](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/23/)
+## [臧曥潣 鞝� 韤挫](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/23/)
### 頃勳殧 臁瓣贝
@@ -224,7 +224,7 @@ weighted avg 0.73 0.72 0.72 1199
臧� 旮办垹鞐愲姅 韸胳渽頃� 靾� 鞛堧姅 毵庫潃 靾橃潣 韺岆澕氙疙劙臧€ 臁挫灛頃╇媹雼�. 臧� 旮半掣 韺岆澕氙疙劙毳� 臁办偓頃橁碃 韺岆澕氙疙劙毳� 臁办爤項れ劀 氇嵏 頀堨鞐� 鞏措枻 鞚橂臧€ 攵€鞐悩電旍 靸濌皝頃╇媹雼�.
-## [臧曥潣 頉� 韤挫](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/24/)
+## [臧曥潣 頉� 韤挫](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/24/)
## 瓴€韱� & 鞛愱赴欤茧弰 頃欖姷
diff --git a/4-Classification/3-Classifiers-2/translations/README.pt-br.md b/4-Classification/3-Classifiers-2/translations/README.pt-br.md
index 4315d9e3..7693c0e8 100644
--- a/4-Classification/3-Classifiers-2/translations/README.pt-br.md
+++ b/4-Classification/3-Classifiers-2/translations/README.pt-br.md
@@ -2,7 +2,7 @@
Nesta segunda li莽茫o de classifica莽茫o, voc锚 explorar谩 outras maneiras de classificar dados num茅ricos. Voc锚 tamb茅m aprender谩 sobre as ramifica莽玫es para escolher um classificador em vez de outro.
-## [Question谩rio inicial](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/23?loc=ptbr)
+## [Question谩rio inicial](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/23?loc=ptbr)
### Pr茅-requisito
@@ -224,7 +224,7 @@ Este m茅todo de arendizado de m谩quina "combina as previs玫es de v谩rios estimad
Cada uma dessas t茅cnicas possui um grande n煤mero de par芒metros. Pesquise os par芒metros padr茫o de cada um e pense no que o ajuste desses par芒metros significaria para a qualidade do modelo.
-## [Question谩rio para fixa莽茫o](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/24?loc=ptbr)
+## [Question谩rio para fixa莽茫o](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/24?loc=ptbr)
## Revis茫o e Auto Aprendizagem
diff --git a/4-Classification/3-Classifiers-2/translations/README.tr.md b/4-Classification/3-Classifiers-2/translations/README.tr.md
index aba0b99b..c0e3aa3d 100644
--- a/4-Classification/3-Classifiers-2/translations/README.tr.md
+++ b/4-Classification/3-Classifiers-2/translations/README.tr.md
@@ -2,7 +2,7 @@
Bu ikinci s谋n谋fland谋rma dersinde, say谋sal veriyi s谋n谋fland谋rmak i莽in daha fazla y枚ntem 枚臒reneceksiniz. Ayr谋ca, bir s谋n谋fland谋r谋c谋y谋 di臒erlerine tercih etmenin sonu莽lar谋n谋 da 枚臒reneceksiniz.
-## [Ders 枚ncesi k谋sa s谋nav谋](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/23/?loc=tr)
+## [Ders 枚ncesi k谋sa s谋nav谋](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/23/?loc=tr)
### 脰n ko艧ul
@@ -224,7 +224,7 @@ Makine 脰臒reniminin bu y枚ntemi, modelin kalitesini art谋rmak i莽in, "bir莽ok t
Bu y枚ntemlerden her biri de臒i艧tirebilece臒iniz birs眉r眉 parametre i莽eriyor. Her birinin varsay谋lan parametrelerini ara艧t谋r谋n ve bu parametreleri de臒i艧tirmenin modelin kalitesi i莽in ne anlama gelebilece臒i hakk谋nda d眉艧眉n眉n.
-## [Ders sonras谋 k谋sa s谋nav谋](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/24/?loc=tr)
+## [Ders sonras谋 k谋sa s谋nav谋](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/24/?loc=tr)
## G枚zden Ge莽irme & Kendi Kendine 脟al谋艧ma
diff --git a/4-Classification/3-Classifiers-2/translations/README.zh-cn.md b/4-Classification/3-Classifiers-2/translations/README.zh-cn.md
index 203daf04..e75877b0 100644
--- a/4-Classification/3-Classifiers-2/translations/README.zh-cn.md
+++ b/4-Classification/3-Classifiers-2/translations/README.zh-cn.md
@@ -2,7 +2,7 @@
鍦ㄧ浜岃妭璇剧▼涓紝鎮ㄥ皢鎺㈢储鏇村鏂规硶鏉ュ鏁板€兼暟鎹繘琛屽垎绫汇€傛偍杩樺皢浜嗚В閫夋嫨涓嶅悓鐨勫垎绫诲櫒鎵€甯︽潵鐨勭粨鏋溿€�
-## [璇惧墠娴嬮獙](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/23/)
+## [璇惧墠娴嬮獙](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/23/)
### 鍏堝喅鏉′欢
@@ -224,7 +224,7 @@ weighted avg 0.73 0.72 0.72 1199
杩欎簺鎶€鏈柟娉曟瘡涓兘鏈夊緢澶氳兘澶熻鎮ㄥ井璋冪殑鍙傛暟銆傜爺绌舵瘡涓€涓殑榛樿鍙傛暟锛屽苟鎬濊€冭皟鏁磋繖浜涘弬鏁板妯″瀷璐ㄩ噺鏈変綍鎰忎箟銆�
-## [璇惧悗娴嬮獙](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/24/)
+## [璇惧悗娴嬮獙](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/24/)
## 鍥為【涓庤嚜瀛�
diff --git a/4-Classification/4-Applied/README.md b/4-Classification/4-Applied/README.md
index baf63da1..2e069631 100644
--- a/4-Classification/4-Applied/README.md
+++ b/4-Classification/4-Applied/README.md
@@ -8,7 +8,7 @@ One of the most useful practical uses of machine learning is building recommenda
> 馃帴 Click the image above for a video: Jen Looper builds a web app using classified cuisine data
-## [Pre-lecture quiz](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/25/)
+## [Pre-lecture quiz](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/25/)
In this lesson you will learn:
@@ -299,7 +299,7 @@ Congratulations, you have created a 'recommendation' web app with a few fields.
Your web app is very minimal, so continue to build it out using ingredients and their indexes from the [ingredient_indexes](../data/ingredient_indexes.csv) data. What flavor combinations work to create a given national dish?
-## [Post-lecture quiz](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/26/)
+## [Post-lecture quiz](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/26/)
## Review & Self Study
diff --git a/4-Classification/4-Applied/translations/README.es.md b/4-Classification/4-Applied/translations/README.es.md
index 161db84c..927227e2 100644
--- a/4-Classification/4-Applied/translations/README.es.md
+++ b/4-Classification/4-Applied/translations/README.es.md
@@ -8,7 +8,7 @@ Uno de los usos pr谩cticos m谩s 煤tiles del aprendizaje autom谩tico es construir
> 馃帴 Haz clic en la imagen de arriba para ver el video: Jen Looper construye una aplicaci贸n web usando los datos clasificados de cocina.
-## [Examen previo a la lecci贸n](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/25?loc=es)
+## [Examen previo a la lecci贸n](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/25?loc=es)
En esta lecci贸n aprender谩s:
@@ -301,7 +301,7 @@ Felicidades, has creado una aplicaci贸n de 'recomendaci贸n' con pocos campos. 隆
Tu aplicaci贸n web es m铆nima, as铆 que continua construy茅ndola usando los ingredientes y sus 铆ndices de los datos [ingredient_indexes](../../data/ingredient_indexes.csv). 驴Qu茅 combinaciones de sabor funcionan para crear un determinado platillo nacional?
-## [Examen posterior a la lecci贸n](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/26?loc=es)
+## [Examen posterior a la lecci贸n](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/26?loc=es)
## Revisi贸n y autoestudio
diff --git a/4-Classification/4-Applied/translations/README.it.md b/4-Classification/4-Applied/translations/README.it.md
index 4e4816dc..cba81d55 100644
--- a/4-Classification/4-Applied/translations/README.it.md
+++ b/4-Classification/4-Applied/translations/README.it.md
@@ -8,7 +8,7 @@ Uno degli usi pratici pi霉 utili dell'apprendimento automatico 猫 la creazione d
> 馃帴 Fare clic sull'immagine sopra per un video
-## [Quiz pre-lezione](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/25/?loc=it)
+## [Quiz pre-lezione](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/25/?loc=it)
In questa lezione, si imparer脿:
@@ -321,7 +321,7 @@ Congratulazioni, si 猫 creato un'app web di "raccomandazione" con pochi campi. S
L'app web 猫 molto minimale, quindi continuare a costruirla usando gli ingredienti e i loro indici dai dati [ingredient_indexes](../../data/ingredient_indexes.csv) . Quali combinazioni di sapori funzionano per creare un determinato piatto nazionale?
-## [Quiz post-lezione](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/26/?loc=it)
+## [Quiz post-lezione](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/26/?loc=it)
## Revisione e Auto Apprendimento
diff --git a/4-Classification/4-Applied/translations/README.ko.md b/4-Classification/4-Applied/translations/README.ko.md
index 3d9e2794..336ecafb 100644
--- a/4-Classification/4-Applied/translations/README.ko.md
+++ b/4-Classification/4-Applied/translations/README.ko.md
@@ -8,7 +8,7 @@
> 馃帴 鞓侅儊 氤措牑氅� 鞚措歆€ 韥措Ν
-## [臧曥潣 鞝� 韤挫](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/25/)
+## [臧曥潣 鞝� 韤挫](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/25/)
鞚� 臧曥潣鞐愳劀 雼れ潓鞚� 氚办毎瓴� 霅╇媹雼�:
@@ -322,7 +322,7 @@ index.html 韺岇澕鞚� 韽措崝鞐愳劀 Visual Studio Code搿� 韯半雱� 靹胳厴鞚� 鞐�
鞚� 鞗� 鞎膘潃 毵れ毎 鞛戩晞靹�, [ingredient_indexes](../../data/ingredient_indexes.csv) 雿办澊韯办棎靹� 靹彪秳瓿� 鞚鸽嵄鞀る 瓿勳啀 毵岆摥雼堧嫟. 欤检柎歆� 甑 鞖旊Μ毳� 毵岆摐霠る┐ 鞏措枻 頀嶋 臁绊暕鞙茧 鞛戩梾頃挫暭 霅橂倶鞖�?
-## [Post-lecture quiz](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/26/)
+## [Post-lecture quiz](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/26/)
## 瓴€韱� & 鞛愱赴欤茧弰 頃欖姷
diff --git a/4-Classification/4-Applied/translations/README.pt-br.md b/4-Classification/4-Applied/translations/README.pt-br.md
index f7fc7214..07c24e39 100644
--- a/4-Classification/4-Applied/translations/README.pt-br.md
+++ b/4-Classification/4-Applied/translations/README.pt-br.md
@@ -8,7 +8,7 @@ Um dos usos pr谩ticos mais 煤teis do aprendizado de m谩quina 茅 criar sistemas d
> 馃帴 Clique na imagem acima para ver um v铆deo
-## [Question谩rio inicial](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/25?loc=ptbr)
+## [Question谩rio inicial](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/25?loc=ptbr)
Nesta li莽茫o voc锚 aprender谩:
@@ -322,7 +322,7 @@ Parab茅ns, voc锚 criou uma aplica莽茫o Web de 'recomenda莽茫o' com alguns campos
Sua aplica莽茫o 茅 simples, portanto, adicione outros ingredientes observando seus 铆ndices na [planilha de ingredientes](../../data/ingredient_indexes.csv). Que combina莽玫es de sabores funcionam para criar um determinado prato?
-## [Question谩rio para fixa莽茫o](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/26?loc=ptbr)
+## [Question谩rio para fixa莽茫o](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/26?loc=ptbr)
## Revis茫o e Auto Aprendizagem
diff --git a/4-Classification/4-Applied/translations/README.tr.md b/4-Classification/4-Applied/translations/README.tr.md
index d30610a1..fea7f0cf 100644
--- a/4-Classification/4-Applied/translations/README.tr.md
+++ b/4-Classification/4-Applied/translations/README.tr.md
@@ -8,7 +8,7 @@ Makine 枚臒reniminin en faydal谋 pratik kullan谋mlar谋ndan biri, 枚nerici/tavsiy
> :movie_camera: Video i莽in yukar谋daki foto臒rafa t谋klay谋n
-## [Ders 枚ncesi k谋sa s谋nav谋](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/25/?loc=tr)
+## [Ders 枚ncesi k谋sa s谋nav谋](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/25/?loc=tr)
Bu derste 艧unlar谋 枚臒reneceksiniz:
@@ -321,7 +321,7 @@ Tebrikler, birka莽 de臒i艧kenle bir '枚nerici' web uygulamas谋 olu艧turdunuz! Bu
Web uygulaman谋z 莽ok minimal, bu y眉zden [ingredient_indexes](../../data/ingredient_indexes.csv) verisinden malzemeleri ve indexlerini kullanarak web uygulaman谋z谋 olu艧turmaya devam edin. Verilen bir ulusal yeme臒i yapmak i莽in hangi tat birle艧imleri i艧e yar谋yor?
-## [Ders sonras谋 k谋sa s谋nav谋](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/26/?loc=tr)
+## [Ders sonras谋 k谋sa s谋nav谋](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/26/?loc=tr)
## G枚zden Ge莽irme & Kendi Kendine 脟al谋艧ma
diff --git a/4-Classification/4-Applied/translations/README.zh-CN.md b/4-Classification/4-Applied/translations/README.zh-CN.md
index eed2a5ca..f39c2907 100644
--- a/4-Classification/4-Applied/translations/README.zh-CN.md
+++ b/4-Classification/4-Applied/translations/README.zh-CN.md
@@ -7,7 +7,7 @@
> 馃帴 鐐瑰嚮涓婇潰鐨勫浘鐗囨煡鐪嬭棰�
-## [璇惧墠娴嬮獙](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/25/)
+## [璇惧墠娴嬮獙](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/25/)
鏈妭璇剧▼涓偍灏嗕細瀛︿範锛�
@@ -318,7 +318,7 @@ Netron 鏄煡鐪嬫偍妯″瀷鐨勬湁鐢ㄥ伐鍏枫€�
鎮ㄧ殑 Web 搴旂敤绋嬪簭杩樺緢灏忓阀锛屾墍浠ョ户缁娇鐢╗閰嶆枡绱㈠紩](../../data/ingredient_indexes.csv)涓殑閰嶆枡鏁版嵁鍜岀储寮曟暟鎹潵鏋勫缓瀹冨惂銆傜敤浠€涔堟牱鐨勫彛鍛崇粍鍚堟墠鑳藉垱閫犲嚭涓€閬撶壒瀹氱殑姘戞棌鑿滆偞锛�
-## [璇惧悗娴嬮獙](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/26/)
+## [璇惧悗娴嬮獙](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/26/)
## 鍥為【涓庤嚜瀛�
diff --git a/5-Clustering/1-Visualize/README.md b/5-Clustering/1-Visualize/README.md
index 32f07019..1e17b15b 100644
--- a/5-Clustering/1-Visualize/README.md
+++ b/5-Clustering/1-Visualize/README.md
@@ -5,7 +5,7 @@ Clustering is a type of [Unsupervised Learning](https://wikipedia.org/wiki/Unsup
[](https://youtu.be/ty2advRiWJM "No One Like You by PSquare")
> 馃帴 Click the image above for a video. While you're studying machine learning with clustering, enjoy some Nigerian Dance Hall tracks - this is a highly rated song from 2014 by PSquare.
-## [Pre-lecture quiz](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/27/)
+## [Pre-lecture quiz](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/27/)
### Introduction
[Clustering](https://link.springer.com/referenceworkentry/10.1007%2F978-0-387-30164-8_124) is very useful for data exploration. Let's see if it can help discover trends and patterns in the way Nigerian audiences consume music.
@@ -317,7 +317,7 @@ In general, for clustering, you can use scatterplots to show clusters of data, s
In preparation for the next lesson, make a chart about the various clustering algorithms you might discover and use in a production environment. What kinds of problems is the clustering trying to address?
-## [Post-lecture quiz](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/28/)
+## [Post-lecture quiz](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/28/)
## Review & Self Study
diff --git a/5-Clustering/1-Visualize/solution/R/lesson_14-R.ipynb b/5-Clustering/1-Visualize/solution/R/lesson_14-R.ipynb
index a1862ba1..c38f6f96 100644
--- a/5-Clustering/1-Visualize/solution/R/lesson_14-R.ipynb
+++ b/5-Clustering/1-Visualize/solution/R/lesson_14-R.ipynb
@@ -7,7 +7,7 @@
"\r\n",
"Clustering is a type of [Unsupervised Learning](https://wikipedia.org/wiki/Unsupervised_learning) that presumes that a dataset is unlabelled or that its inputs are not matched with predefined outputs. It uses various algorithms to sort through unlabeled data and provide groupings according to patterns it discerns in the data.\r\n",
"\r\n",
- "[**Pre-lecture quiz**](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/27/)\r\n",
+ "[**Pre-lecture quiz**](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/27/)\r\n",
"\r\n",
"### **Introduction**\r\n",
"\r\n",
@@ -439,7 +439,7 @@
"\n",
"In preparation for the next lesson, make a chart about the various clustering algorithms you might discover and use in a production environment. What kinds of problems is the clustering trying to address?\n",
"\n",
- "## [**Post-lecture quiz**](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/28/)\n",
+ "## [**Post-lecture quiz**](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/28/)\n",
"\n",
"## **Review & Self Study**\n",
"\n",
diff --git a/5-Clustering/1-Visualize/solution/R/lesson_14.Rmd b/5-Clustering/1-Visualize/solution/R/lesson_14.Rmd
index 53c0cd21..0eb57173 100644
--- a/5-Clustering/1-Visualize/solution/R/lesson_14.Rmd
+++ b/5-Clustering/1-Visualize/solution/R/lesson_14.Rmd
@@ -14,7 +14,7 @@ output:
Clustering is a type of [Unsupervised Learning](https://wikipedia.org/wiki/Unsupervised_learning) that presumes that a dataset is unlabelled or that its inputs are not matched with predefined outputs. It uses various algorithms to sort through unlabeled data and provide groupings according to patterns it discerns in the data.
-[**Pre-lecture quiz**](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/27/)
+[**Pre-lecture quiz**](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/27/)
### **Introduction**
@@ -315,7 +315,7 @@ In general, for clustering, you can use scatterplots to show clusters of data, s
In preparation for the next lesson, make a chart about the various clustering algorithms you might discover and use in a production environment. What kinds of problems is the clustering trying to address?
-## [**Post-lecture quiz**](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/28/)
+## [**Post-lecture quiz**](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/28/)
## **Review & Self Study**
diff --git a/5-Clustering/1-Visualize/translations/README.es.md b/5-Clustering/1-Visualize/translations/README.es.md
index 852aa445..d46894e6 100644
--- a/5-Clustering/1-Visualize/translations/README.es.md
+++ b/5-Clustering/1-Visualize/translations/README.es.md
@@ -6,7 +6,7 @@ El agrupamiento (clustering) es un tipo de [aprendizaje no supervisado](https://
> 馃帴 Haz clic en la imagen de arriba para ver el video. Mientras estudias aprendizaje autom谩tico con agrupamiento, disfruta de algunas canciones Dance Hall Nigerianas - esta es una canci贸n muy popular del 2014 de PSquare.
-## [Examen previo a la lecci贸n](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/27?loc=es)
+## [Examen previo a la lecci贸n](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/27?loc=es)
### Introducci贸n
@@ -320,7 +320,7 @@ En general, para el agrupamiento, puedes usar gr谩ficos de dispersi贸n para most
En preparaci贸n para la siguiente lecci贸n, realiza una gr谩fica acerca de los diverso algoritmos de agrupamiento que puedes descubrir y usar en un ambiente de producci贸n. 驴Qu茅 tipo de problemas trata de abordar el agrupamiento?
-## [Examen porterior a la lecci贸n](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/28?loc=es)
+## [Examen porterior a la lecci贸n](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/28?loc=es)
## Revisi贸n y auto-estudio
diff --git a/5-Clustering/1-Visualize/translations/README.it.md b/5-Clustering/1-Visualize/translations/README.it.md
index 1c9d8323..e3770789 100644
--- a/5-Clustering/1-Visualize/translations/README.it.md
+++ b/5-Clustering/1-Visualize/translations/README.it.md
@@ -5,7 +5,7 @@ Il clustering 猫 un tipo di [apprendimento non supervisionato](https://wikipedia
[](https://youtu.be/ty2advRiWJM "No One Like You di PSquare")
> 馃帴 Fare clic sull'immagine sopra per un video. Mentre si studia machine learning con il clustering, si potranno gradire brani della Nigerian Dance Hall: questa 猫 una canzone molto apprezzata del 2014 di PSquare.
-## [Quiz pre-lezione](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/27/?loc=it)
+## [Quiz pre-lezione](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/27/?loc=it)
### Introduzione
@@ -319,7 +319,7 @@ In generale, per il clustering 猫 possibile utilizzare i grafici a dispersione p
In preparazione per la lezione successiva, creare un grafico sui vari algoritmi di clustering che si potrebbero scoprire e utilizzare in un ambiente di produzione. Che tipo di problemi sta cercando di affrontare il clustering?
-## [Quiz post-lezione](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/28/?loc=it)
+## [Quiz post-lezione](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/28/?loc=it)
## Revisione e Auto Apprendimento
diff --git a/5-Clustering/1-Visualize/translations/README.ko.md b/5-Clustering/1-Visualize/translations/README.ko.md
index 8f82c2a0..b6a36b20 100644
--- a/5-Clustering/1-Visualize/translations/README.ko.md
+++ b/5-Clustering/1-Visualize/translations/README.ko.md
@@ -6,7 +6,7 @@ Clustering鞚� 雿办澊韯办厠鞐� 霛茧波鞚� 攵欖澊歆€ 鞎婈卑雮� 鞛呺牓鞚� 氙鸽Μ 鞝�
> 馃帴 鞓侅儊鞚� 氤措牑氅� 鞚措歆€ 韥措Ν. While you're studying machine learning with clustering, enjoy some Nigerian Dance Hall tracks - this is a highly rated song from 2014 by PSquare.
-## [臧曥潣 鞝� 韤挫](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/27/)
+## [臧曥潣 鞝� 韤挫](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/27/)
### 靻岅皽
@@ -322,7 +322,7 @@ Clustering鞚� 雿办澊韯办厠鞐� 霛茧波鞚� 攵欖澊歆€ 鞎婈卑雮� 鞛呺牓鞚� 氙鸽Μ 鞝�
雼れ潓 臧曥潣毳� 欷€牍勴晿旮� 鞙勴暣靹�, 頂勲雿曥厴 頇橁步鞐愳劀 彀眷晞靹� 靷毄頃� 靾� 鞛堧姅 雼れ枒頃� clustering 鞎岅碃毽鞚� 彀姼搿� 毵岆摥雼堧嫟. clustering鞚€ 鞏措枻 氍胳牅毳� 頃搓舶頃橂牑瓿� 鞁滊弰頃橂倶鞖�?
-## [臧曥潣 頉� 韤挫](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/28/)
+## [臧曥潣 頉� 韤挫](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/28/)
## 瓴€韱� & 鞛愱赴欤茧弰 頃欖姷
diff --git a/5-Clustering/1-Visualize/translations/README.zh-cn.md b/5-Clustering/1-Visualize/translations/README.zh-cn.md
index e5a557eb..98d22db4 100644
--- a/5-Clustering/1-Visualize/translations/README.zh-cn.md
+++ b/5-Clustering/1-Visualize/translations/README.zh-cn.md
@@ -6,7 +6,7 @@
> 馃帴 鐐瑰嚮涓婇潰鐨勫浘鐗囪鐪嬭棰戙€傚綋鎮ㄩ€氳繃鑱氱被瀛︿範鏈哄櫒瀛︿範鏃讹紝璇锋璧忎竴浜涘凹鏃ュ埄浜氳垶鍘呮洸鐩� - 杩欐槸 2014 骞� PSquare 涓婇珮搴﹁瘎浠风殑姝屾洸銆�
-## [璇惧墠娴嬮獙](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/27/)
+## [璇惧墠娴嬮獙](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/27/)
### 浠嬬粛
@@ -326,7 +326,7 @@
鑱氱被璇曞浘瑙ｅ喅浠€涔堟牱鐨勯棶棰橈紵
-## [璇惧悗娴嬮獙](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/28/)
+## [璇惧悗娴嬮獙](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/28/)
## 澶嶄範涓庤嚜瀛�
diff --git a/5-Clustering/2-K-Means/README.md b/5-Clustering/2-K-Means/README.md
index deb2037f..628ecbb1 100644
--- a/5-Clustering/2-K-Means/README.md
+++ b/5-Clustering/2-K-Means/README.md
@@ -4,7 +4,7 @@
> 馃帴 Click the image above for a video: Andrew Ng explains clustering
-## [Pre-lecture quiz](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/29/)
+## [Pre-lecture quiz](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/29/)
In this lesson, you will learn how to create clusters using Scikit-learn and the Nigerian music dataset you imported earlier. We will cover the basics of K-Means for Clustering. Keep in mind that, as you learned in the earlier lesson, there are many ways to work with clusters and the method you use depends on your data. We will try K-Means as it's the most common clustering technique. Let's get started!
@@ -238,7 +238,7 @@ Spend some time with this notebook, tweaking parameters. Can you improve the acc
Hint: Try to scale your data. There's commented code in the notebook that adds standard scaling to make the data columns resemble each other more closely in terms of range. You'll find that while the silhouette score goes down, the 'kink' in the elbow graph smooths out. This is because leaving the data unscaled allows data with less variance to carry more weight. Read a bit more on this problem [here](https://stats.stackexchange.com/questions/21222/are-mean-normalization-and-feature-scaling-needed-for-k-means-clustering/21226#21226).
-## [Post-lecture quiz](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/30/)
+## [Post-lecture quiz](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/30/)
## Review & Self Study
diff --git a/5-Clustering/2-K-Means/solution/R/lesson_15-R.ipynb b/5-Clustering/2-K-Means/solution/R/lesson_15-R.ipynb
index 88461240..9ccc82d3 100644
--- a/5-Clustering/2-K-Means/solution/R/lesson_15-R.ipynb
+++ b/5-Clustering/2-K-Means/solution/R/lesson_15-R.ipynb
@@ -32,7 +32,7 @@
"source": [
"## Explore K-Means clustering using R and Tidy data principles.\n",
"\n",
- "### [**Pre-lecture quiz**](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/29/)\n",
+ "### [**Pre-lecture quiz**](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/29/)\n",
"\n",
"In this lesson, you will learn how to create clusters using the Tidymodels package and other packages in the R ecosystem (we'll call them friends 馃鈥嶐煠濃€嶐煣�), and the Nigerian music dataset you imported earlier. We will cover the basics of K-Means for Clustering. Keep in mind that, as you learned in the earlier lesson, there are many ways to work with clusters and the method you use depends on your data. We will try K-Means as it's the most common clustering technique. Let's get started!\n",
"\n",
@@ -593,7 +593,7 @@
"\n",
"Hint: Try to scale your data. There's commented code in the notebook that adds standard scaling to make the data columns resemble each other more closely in terms of range. You'll find that while the silhouette score goes down, the 'kink' in the elbow graph smooths out. This is because leaving the data unscaled allows data with less variance to carry more weight. Read a bit more on this problem [here](https://stats.stackexchange.com/questions/21222/are-mean-normalization-and-feature-scaling-needed-for-k-means-clustering/21226#21226).\n",
"\n",
- "## [**Post-lecture quiz**](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/30/)\n",
+ "## [**Post-lecture quiz**](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/30/)\n",
"\n",
"## **Review & Self Study**\n",
"\n",
diff --git a/5-Clustering/2-K-Means/solution/R/lesson_15.Rmd b/5-Clustering/2-K-Means/solution/R/lesson_15.Rmd
index 691262b7..61f7869a 100644
--- a/5-Clustering/2-K-Means/solution/R/lesson_15.Rmd
+++ b/5-Clustering/2-K-Means/solution/R/lesson_15.Rmd
@@ -13,7 +13,7 @@ output:
## Explore K-Means clustering using R and Tidy data principles.
-### [**Pre-lecture quiz**](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/29/)
+### [**Pre-lecture quiz**](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/29/)
In this lesson, you will learn how to create clusters using the Tidymodels package and other packages in the R ecosystem (we'll call them friends 馃鈥嶐煠濃€嶐煣�), and the Nigerian music dataset you imported earlier. We will cover the basics of K-Means for Clustering. Keep in mind that, as you learned in the earlier lesson, there are many ways to work with clusters and the method you use depends on your data. We will try K-Means as it's the most common clustering technique. Let's get started!
@@ -353,7 +353,7 @@ Spend some time with this notebook, tweaking parameters. Can you improve the acc
Hint: Try to scale your data. There's commented code in the notebook that adds standard scaling to make the data columns resemble each other more closely in terms of range. You'll find that while the silhouette score goes down, the 'kink' in the elbow graph smooths out. This is because leaving the data unscaled allows data with less variance to carry more weight. Read a bit more on this problem [here](https://stats.stackexchange.com/questions/21222/are-mean-normalization-and-feature-scaling-needed-for-k-means-clustering/21226#21226).
-## [**Post-lecture quiz**](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/30/)
+## [**Post-lecture quiz**](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/30/)
## **Review & Self Study**
diff --git a/5-Clustering/2-K-Means/translations/README.es.md b/5-Clustering/2-K-Means/translations/README.es.md
index 3bf18c08..2d83b395 100644
--- a/5-Clustering/2-K-Means/translations/README.es.md
+++ b/5-Clustering/2-K-Means/translations/README.es.md
@@ -4,7 +4,7 @@
> 馃帴 Haz clic en la imagen de arriba para ver el video: Andrew Ng explica el agrupamiento"
-## [Examen previo a la lecci贸n](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/29?loc=es)
+## [Examen previo a la lecci贸n](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/29?loc=es)
En esta lecci贸n, aprender谩s c贸mo crear grupos usando Scikit-learn y el conjunto de datos de m煤sica Nigeriana que importaste anteriormente. Cubriremos los conceptos b谩sicos de K-Medias para agrupamiento. Ten en mente que, como aprendiste en lecciones anteriores, hay muchas formas de de trabajar con grupos y el m茅todo que uses depende de tus datos. Probaremos K-medias ya que es la t茅cnica de agrupamiento m谩s com煤n. 隆Comencemos!
@@ -238,7 +238,7 @@ Dedica algo de tiempo a este notebook, ajustando los par谩metros. 驴Puedes mejor
Pista: Prueba escalar tus datos. Hay c贸digo comentado en el notebook que agrega escalado est谩ndar para hacer que las columnas de datos se parezcan m谩s entre s铆 en t茅rminos de rango. Encontrar谩s que mientras el puntaje de silueta disminuye el 'pliegue' en la gr谩fica de codo se suaviza. Esto es por qu茅 al dejar los datos sin escalar le permite a los datos con menos variaci贸n tengan m谩s peso. Lee un poco m谩s de este problema [aqu铆](https://stats.stackexchange.com/questions/21222/are-mean-normalization-and-feature-scaling-needed-for-k-means-clustering/21226#21226).
-## [Examen posterior a la lecci贸n](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/30?loc=es)
+## [Examen posterior a la lecci贸n](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/30?loc=es)
## Revisi贸n y auto-estudio
diff --git a/5-Clustering/2-K-Means/translations/README.it.md b/5-Clustering/2-K-Means/translations/README.it.md
index 02829606..42ef5750 100644
--- a/5-Clustering/2-K-Means/translations/README.it.md
+++ b/5-Clustering/2-K-Means/translations/README.it.md
@@ -4,7 +4,7 @@
> 馃帴 Fare clic sull'immagine sopra per un video: Andrew Ng spiega il clustering
-## [Quiz pre-lezione](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/29/?loc=it)
+## [Quiz pre-lezione](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/29/?loc=it)
In questa lezione si imparer脿 come creare cluster utilizzando Scikit-learn e l'insieme di dati di musica nigeriana importato in precedenza. Si tratteranno le basi di K-Means per Clustering. Si tenga presente che, come appreso nella lezione precedente, ci sono molti modi per lavorare con i cluster e il metodo usato dipende dai propri dati. Si prover脿 K-Means poich茅 猫 la tecnica di clustering pi霉 comune. Si inizia!
@@ -238,7 +238,7 @@ Trascorrere un po' di tempo con questo notebook, modificando i parametri. E poss
Suggerimento: provare a ridimensionare i dati. C'猫 un codice commentato nel notebook che aggiunge il ridimensionamento standard per rendere le colonne di dati pi霉 simili tra loro in termini di intervallo. Si scoprir脿 che mentre il punteggio della silhouette diminuisce, il "kink" nel grafico del gomito si attenua. Questo perch茅 lasciare i dati non scalati consente ai dati con meno varianza di avere pi霉 peso. Leggere un po' di pi霉 su questo problema [qui](https://stats.stackexchange.com/questions/21222/are-mean-normalization-and-feature-scaling-needed-for-k-means-clustering/21226#21226).
-## [Quiz post-lezione](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/30/?loc=it)
+## [Quiz post-lezione](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/30/?loc=it)
## Revisione e Auto Apprendimento
diff --git a/5-Clustering/2-K-Means/translations/README.ko.md b/5-Clustering/2-K-Means/translations/README.ko.md
index d4ee91c7..4d5d6ca7 100644
--- a/5-Clustering/2-K-Means/translations/README.ko.md
+++ b/5-Clustering/2-K-Means/translations/README.ko.md
@@ -4,7 +4,7 @@
> 馃帴 鞓侅儊鞚� 氤措牑氅� 鞚措歆€ 韥措Ν: Andrew Ng explains clustering
-## [臧曥潣 鞝� 韤挫](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/29/)
+## [臧曥潣 鞝� 韤挫](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/29/)
鞚� 臧曥潣鞐愳劀, Scikit-learn瓿� 頃粯 鞚挫爠鞐� 臧€鞝胳槰 雮橃澊歆€毽晞 鞚岇晠 雿办澊韯办厠鞙茧 韥措煬鞀ろ劙 鞝滌瀾 氚╈嫕鞚� 氚办毟 鞓堨爼鞛呺媹雼�. Clustering鞚� 鞙勴暅 K-Means 旮办磮毳� 雼る（瓴� 霅╇媹雼�. 彀戈碃搿�, 鞚挫爠 臧曥潣鞐愳劀 氚办洜雿橂寑搿�, 韥措煬鞀ろ劙搿� 鞛戩梾頃橂姅 鞐煬 氚╈嫕鞚� 鞛堦碃 雿办澊韯半ゼ 旮半皹頃� 氚╈嫕霃� 鞛堨姷雼堧嫟. 臧€鞛� 鞚茧皹鞝� clustering 旮办垹鞚� K-Means鞚� 鞁滊弰頃措炒霠り碃 頃╇媹雼�. 鞁滌瀾頃措磪雼堧嫟!
@@ -238,7 +238,7 @@ Variance電� "the average of the squared differences from the Mean."鞙茧 鞝曥潣
頌岉姼: 雿办澊韯半ゼ 雿� 韨れ泴氪呺媹雼�. 臧€旯岇毚 氩旍渼 臁瓣贝鞐� 牍勳姺頃� 雿办澊韯� 鞐挫潉 毵岆摛瓿犾瀽 於旉皜頃橂姅 響滌 鞀れ紑鞚茧 旖旊摐毳� 雲疙姼攵侅棎 欤检劃鞙茧 雮布鞀惦媹雼�. silhouette 鞝愳垬臧€ 雮晞歆€電� 霃欖晥, elbow 攴鸽灅頂勳潣 'kink'臧€ 欤茧 韼挫電� 瓴冹潉 氤� 靾� 鞛堨姷雼堧嫟. 雿办澊韯半ゼ 臁办爼頃橃 鞎婈碃 雮赴氅� 雿� 攵勳偘霅� 雿办澊韯瓣皜 雿� 毵庫潃 臧€欷戩箻搿� 雮橂ゼ 靾� 鞛堧嫟電� 鞚挫湢鞛呺媹雼�. [here](https://stats.stackexchange.com/questions/21222/are-mean-normalization-and-feature-scaling-needed-for-k-means-clustering/21226#21226) 鞚� 氍胳牅毳� 臁瓣笀 雿� 鞚届柎氪呺媹雼�.
-## [臧曥潣 頉� 韤挫](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/30/)
+## [臧曥潣 頉� 韤挫](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/30/)
## 瓴€韱� & 鞛愱赴欤茧弰 頃欖姷
diff --git a/5-Clustering/2-K-Means/translations/README.zh-cn.md b/5-Clustering/2-K-Means/translations/README.zh-cn.md
index efabf8c1..3a9fba1c 100644
--- a/5-Clustering/2-K-Means/translations/README.zh-cn.md
+++ b/5-Clustering/2-K-Means/translations/README.zh-cn.md
@@ -4,7 +4,7 @@
> 馃帴 鍗曞嚮涓婂浘瑙傜湅瑙嗛锛欰ndrew Ng 瑙ｉ噴鑱氱被
-## [璇惧墠娴嬮獙](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/29/)
+## [璇惧墠娴嬮獙](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/29/)
鍦ㄦ湰璇句腑锛屾偍灏嗗涔犲浣曚娇鐢� Scikit-learn 鍜屾偍涔嬪墠瀵煎叆鐨勫凹鏃ュ埄浜氶煶涔愭暟鎹泦鍒涘缓鑱氱被銆傛垜浠皢浠嬬粛 K-Means 鑱氱被 鐨勫熀纭€鐭ヨ瘑銆傝璁颁綇锛屾濡傛偍鍦ㄤ笂涓€璇句腑瀛﹀埌鐨勶紝浣跨敤鑱氱被鐨勬柟娉曟湁寰堝绉嶏紝鎮ㄤ娇鐢ㄧ殑鏂规硶鍙栧喅浜庢偍鐨勬暟鎹€傛垜浠皢灏濊瘯 K-Means锛屽洜涓哄畠鏄渶甯歌鐨勮仛绫绘妧鏈€傝鎴戜滑寮€濮嬪惂锛�
@@ -239,7 +239,7 @@ K-Means 鑱氱被杩囩▼[鍒嗕笁姝ユ墽琛宂(https://scikit-learn.org/stable/modules/cl
鎻愮ず锛氬皾璇曠缉鏀炬偍鐨勬暟鎹€傜瑪璁版湰涓殑娉ㄩ噴浠ｇ爜娣诲姞浜嗘爣鍑嗙缉鏀撅紝浣挎暟鎹垪鍦ㄨ寖鍥存柟闈㈡洿鍔犵浉浼笺€傛偍浼氬彂鐜帮紝褰撹疆寤撳垎鏁颁笅闄嶆椂锛岃倶閮ㄥ浘涓殑鈥滄壄缁撯€濆彉寰楀钩婊戙€傝繖鏄洜涓轰笉缂╂斁鏁版嵁鍙互璁╂柟宸緝灏忕殑鏁版嵁鎵胯浇鏇村鐨勬潈閲嶃€傚湪[杩欓噷](https://stats.stackexchange.com/questions/21222/are-mean-normalization-and-feature-scaling-needed-for-k-means-clustering/21226#21226)闃呰鏇村鍏充簬杩欎釜闂鐨刐淇℃伅](https://stats.stackexchange.com/questions/21222/are-mean-normalization-and-feature-scaling-needed-for-k-means-clustering/21226#21226)銆�
-## [璇惧悗娴嬮獙](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/30/)
+## [璇惧悗娴嬮獙](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/30/)
## 澶嶄範涓庤嚜瀛�
diff --git a/6-NLP/1-Introduction-to-NLP/README.md b/6-NLP/1-Introduction-to-NLP/README.md
index 571d7f68..ff16307d 100644
--- a/6-NLP/1-Introduction-to-NLP/README.md
+++ b/6-NLP/1-Introduction-to-NLP/README.md
@@ -2,7 +2,7 @@
This lesson covers a brief history and important concepts of *natural language processing*, a subfield of *computational linguistics*.
-## [Pre-lecture quiz](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/31/)
+## [Pre-lecture quiz](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/31/)
## Introduction
@@ -149,7 +149,7 @@ Choose one of the "stop and consider" elements above and either try to implement
In the next lesson, you'll learn about a number of other approaches to parsing natural language and machine learning.
-## [Post-lecture quiz](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/32/)
+## [Post-lecture quiz](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/32/)
## Review & Self Study
diff --git a/6-NLP/1-Introduction-to-NLP/translations/README.es.md b/6-NLP/1-Introduction-to-NLP/translations/README.es.md
index 00d156f4..27f257da 100644
--- a/6-NLP/1-Introduction-to-NLP/translations/README.es.md
+++ b/6-NLP/1-Introduction-to-NLP/translations/README.es.md
@@ -2,7 +2,7 @@
Esta lecci贸n cubre una breve historia y conceptos importante del *procesamiento del lenguaje natural*, un subcampo de la *lig眉铆stica computacional*.
-## [Examen previo a la lecci贸n](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/31?loc=es)
+## [Examen previo a la lecci贸n](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/31?loc=es)
## Introducci贸n
@@ -150,7 +150,7 @@ Elige uno de los elementos "Detente y considera" de arriba y trata de implementa
En la siguiente lecci贸n, aprender谩s acerca de otros enfoques de c贸mo analizar el lenguaje natural y aprendizaje autom谩tico.
-## [Examen posterior a la lecci贸n](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/32?loc=es)
+## [Examen posterior a la lecci贸n](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/32?loc=es)
## Revisi贸n y autoestudio
diff --git a/6-NLP/1-Introduction-to-NLP/translations/README.it.md b/6-NLP/1-Introduction-to-NLP/translations/README.it.md
index 938b979c..58cfc4cf 100644
--- a/6-NLP/1-Introduction-to-NLP/translations/README.it.md
+++ b/6-NLP/1-Introduction-to-NLP/translations/README.it.md
@@ -2,7 +2,7 @@
Questa lezione copre una breve storia e concetti importanti dell' *elaborazione del linguaggio naturale*, un sottocampo della *linguistica computazionale*.
-## [Quiz pre-lezione](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/31/?loc=it)
+## [Quiz pre-lezione](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/31/?loc=it)
## Introduzione
@@ -149,7 +149,7 @@ Scegliere uno degli elementi "fermarsi e riflettere" qui sopra e provare a imple
Nella prossima lezione si impareranno una serie di altri approcci all'analisi del linguaggio naturale e dell'machine learning.
-## [Quiz post-lezione](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/32/?loc=it)
+## [Quiz post-lezione](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/32/?loc=it)
## Revisione e Auto Apprendimento
diff --git a/6-NLP/1-Introduction-to-NLP/translations/README.ko.md b/6-NLP/1-Introduction-to-NLP/translations/README.ko.md
index bde562c4..46e648fd 100644
--- a/6-NLP/1-Introduction-to-NLP/translations/README.ko.md
+++ b/6-NLP/1-Introduction-to-NLP/translations/README.ko.md
@@ -2,7 +2,7 @@
鞚� 臧曥潣鞎犾劀 *computational linguistics* 頃橃渼鞚�, *natural language processing*鞚� 臧勲嫧頃� 鞐偓鞕€ 欷戩殧 旎厜鞚� 雼る９雼堧嫟.
-## [臧曥潣 鞝� 韤挫](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/31/)
+## [臧曥潣 鞝� 韤挫](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/31/)
## 靻岅皽
@@ -149,7 +149,7 @@ Eliza鞕€ 臧欖潃, 雽€頇� 氪囲潃, 靷毄鞛� 鞛呺牓鞚� 鞙犽弰頃挫劀 歆€電レ爜鞙茧
雼れ潓 臧曥潣鞐愳劀, natural language鞕€ 毹胳嫚霟嫕鞚� 攵勳劃頃橂姅 鞐煬 雼るジ 鞝戧芳 氚╈嫕鞐� 雽€頃� 氚办毟 鞓堨爼鞛呺媹雼�.
-## [臧曥潣 頉� 韤挫](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/32/)
+## [臧曥潣 頉� 韤挫](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/32/)
## 瓴€韱� & 鞛愱赴欤茧弰 頃欖姷
diff --git a/6-NLP/1-Introduction-to-NLP/translations/README.pt-br.md b/6-NLP/1-Introduction-to-NLP/translations/README.pt-br.md
index 70917c8d..20d26363 100644
--- a/6-NLP/1-Introduction-to-NLP/translations/README.pt-br.md
+++ b/6-NLP/1-Introduction-to-NLP/translations/README.pt-br.md
@@ -2,7 +2,7 @@
Esta aula cobre uma breve hist贸ria, bem como conceitos importantes do *processamento de linguagem natural*, uma sub谩rea da *Lingu铆stica computacional*.
-## [Teste pr茅-aula](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/31?loc=ptbr)
+## [Teste pr茅-aula](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/31?loc=ptbr)
## Introdu莽茫o
@@ -157,7 +157,7 @@ Escolha um dos elementos do "pare e considere" acima e tente implement谩-lo em c
Na pr贸xima aula, voc锚 ir谩 aprender sobre algumas outras abordagens de an谩lise sint谩tica de linguagem natural e de aprendizado de m谩quina.
-## [Teste p贸s-aula](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/32?loc=ptbr)
+## [Teste p贸s-aula](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/32?loc=ptbr)
## Revis茫o & Autoestudo
diff --git a/6-NLP/1-Introduction-to-NLP/translations/README.zh-cn.md b/6-NLP/1-Introduction-to-NLP/translations/README.zh-cn.md
index b083d7a9..41bda36c 100644
--- a/6-NLP/1-Introduction-to-NLP/translations/README.zh-cn.md
+++ b/6-NLP/1-Introduction-to-NLP/translations/README.zh-cn.md
@@ -1,7 +1,7 @@
# 鑷劧璇█澶勭悊浠嬬粛
杩欒妭璇捐瑙ｄ簡 *鑷劧璇█澶勭悊* 鐨勭畝瑕佸巻鍙插拰閲嶈姒傚康锛�*鑷劧璇█澶勭悊*鏄绠楄瑷€瀛︾殑涓€涓瓙棰嗗煙銆�
-## [璇惧墠娴嬮獙](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/31/)
+## [璇惧墠娴嬮獙](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/31/)
## 浠嬬粛
浼楁墍鍛ㄧ煡锛岃嚜鐒惰瑷€澶勭悊锛圢atural Language Processing, NLP锛夋槸鏈哄櫒瀛︿範鍦ㄧ敓浜ц蒋浠朵腑搴旂敤鏈€骞挎硾鐨勯鍩熶箣涓€銆�
@@ -147,7 +147,7 @@
鍦ㄤ笅涓€璇句腑锛屾偍灏嗕簡瑙ｈВ鏋愯嚜鐒惰瑷€鍜屾満鍣ㄥ涔犵殑璁稿鍏朵粬鏂规硶銆�
-## [璇惧悗娴嬮獙](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/32/)
+## [璇惧悗娴嬮獙](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/32/)
## 澶嶄範涓庤嚜瀛�
diff --git a/6-NLP/2-Tasks/README.md b/6-NLP/2-Tasks/README.md
index a6ee9350..c29033b4 100644
--- a/6-NLP/2-Tasks/README.md
+++ b/6-NLP/2-Tasks/README.md
@@ -2,7 +2,7 @@
For most *natural language processing* tasks, the text to be processed, must be broken down, examined, and the results stored or cross referenced with rules and data sets. These tasks, allows the programmer to derive the _meaning_ or _intent_ or only the _frequency_ of terms and words in a text.
-## [Pre-lecture quiz](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/33/)
+## [Pre-lecture quiz](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/33/)
Let's discover common techniques used in processing text. Combined with machine learning, these techniques help you to analyse large amounts of text efficiently. Before applying ML to these tasks, however, let's understand the problems encountered by an NLP specialist.
@@ -203,7 +203,7 @@ Implement the bot in the prior knowledge check and test it on a friend. Can it t
Take a task in the prior knowledge check and try to implement it. Test the bot on a friend. Can it trick them? Can you make your bot more 'believable?'
-## [Post-lecture quiz](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/34/)
+## [Post-lecture quiz](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/34/)
## Review & Self Study
diff --git a/6-NLP/2-Tasks/translations/README.es.md b/6-NLP/2-Tasks/translations/README.es.md
index 02b04ff7..60cb290a 100644
--- a/6-NLP/2-Tasks/translations/README.es.md
+++ b/6-NLP/2-Tasks/translations/README.es.md
@@ -2,7 +2,7 @@
Para la mayor铆a de tareas de *procesamiento del lenguaje natural*, el texto a ser procesado debe ser partido en bloques, examinado y los resultados almacenados y tener referencias cruzadas con reglas y conjuntos de datos. Esta tareas, le permiten al programador obtener el _significado_, _intenci贸n_ o s贸lo la _frecuencia_ de los t茅rminos y palabras en un texto.
-## [Examen previo a la lecci贸n](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/33?loc=es)
+## [Examen previo a la lecci贸n](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/33?loc=es)
Descubramos t茅cnicas comunes usadas en el procesamiento de texto. Combinadas con el aprendizaje autom谩tico, estas t茅cnicas te ayudan a analizar grandes cantidades de texto de forma eficiente, Antes de aplicar aprendizaje autom谩tico a estas tareas, primero entendamos los problemas encontrados por un especialista del procesamiento del lenguaje natural.
@@ -203,7 +203,7 @@ Implementa el bot con la revisi贸n de conocimiento anterior y pru茅balo con un a
Toma una tarea de la revisi贸n de conocimiento previo y trata de implementarla. Prueba el bot con un amigo. 驴Pudo enga帽arlo? 驴Puedes hacer a tu bot m谩s 'cre铆ble'?
-## [Examen posterior a la lectura](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/34?loc=es)
+## [Examen posterior a la lectura](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/34?loc=es)
## Revisi贸n y autoestudio
diff --git a/6-NLP/2-Tasks/translations/README.it.md b/6-NLP/2-Tasks/translations/README.it.md
index f8f27494..43bc6520 100644
--- a/6-NLP/2-Tasks/translations/README.it.md
+++ b/6-NLP/2-Tasks/translations/README.it.md
@@ -2,7 +2,7 @@
Per la maggior parte delle attivit脿 di *elaborazione del linguaggio naturale* , il testo da elaborare deve essere suddiviso, esaminato e i risultati archiviati o incrociati con regole e insiemi di dati. Queste attivit脿 consentono al programmatore di derivare il _significato_ o l'_intento_ o solo la _frequenza_ di termini e parole in un testo.
-## [Quiz pre-lezione](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/33/?loc=it)
+## [Quiz pre-lezione](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/33/?loc=it)
Si esaminano le comuni tecniche utilizzate nell'elaborazione del testo. Combinate con machine learning, queste tecniche aiutano ad analizzare grandi quantit脿 di testo in modo efficiente. Prima di applicare machine learning a queste attivit脿, tuttavia, occorre cercare di comprendere i problemi incontrati da uno specialista in NLP.
@@ -203,7 +203,7 @@ Implementare il bot nel controllo delle conoscenze precedenti e testarlo su un a
Prendere un'attivit脿 dalla verifica delle conoscenze qui sopra e provare a implementarla. Provare il bot su un amico. Pu貌 ingannarlo? Si pu貌 rendere il bot pi霉 'credibile?'
-## [Quiz post-lezione](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/34/?loc=it)
+## [Quiz post-lezione](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/34/?loc=it)
## Revisione e Auto Apprendimento
diff --git a/6-NLP/2-Tasks/translations/README.ko.md b/6-NLP/2-Tasks/translations/README.ko.md
index de8e7792..63e9ca63 100644
--- a/6-NLP/2-Tasks/translations/README.ko.md
+++ b/6-NLP/2-Tasks/translations/README.ko.md
@@ -2,7 +2,7 @@
雽€攵€攵� *natural language processing* 鞛戩梾鞙茧, 觳橂Μ頃� 韰嶌姢韸鸽ゼ 攵勴暣頃橁碃, 瓴€靷晿瓿�, 攴鸽Μ瓿� 瓴瓣臣毳� 鞝€鞛ロ晿瓯半倶 耄瓣臣 雿办澊韯办厠鞚� 靹滊 彀胳“頄堨姷雼堧嫟. 鞚� 鞛戩梾霌る, 頂勲攴鸽灅毹戈皜 _meaning_ 霕愲姅 _intent_ 霕愲姅 鞓れ 韰嶌姢韸胳棎 鞛堧姅 鞖╈柎鞕€ 雼柎鞚� _frequency_ 毵� 雭岇柎雮� 靾� 鞛堦矊 頃╇媹雼�.
-## [臧曥潣 鞝� 韤挫](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/33/)
+## [臧曥潣 鞝� 韤挫](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/33/)
韰嶌姢韸鸽ゼ 觳橂Μ頃橂┌ 靷毄頄堧崢 鞚茧皹鞝侅澑 旮办垹鞚� 彀眷晞氪呺媹雼�. 毹胳嫚霟嫕鞐� 瓴绊暕霅�, 鞚� 旮办垹鞚€ 須湪鞝侅溂搿� 毵庫潃 韰嶌姢韸鸽ゼ 攵勳劃頃橂姅雿� 霃勳檧欷嶋媹雼�. 攴鸽煬雮�, 鞚� 鞛戩梾鞐� ML鞚� 鞝侅毄頃橁赴 鞝勳棎, NLP 鞀ろ帢靺滊Μ鞀ろ姼臧€ 鞚检溂韨� 氍胳牅毳� 鞚错暣頃╇媹雼�.
@@ -203,7 +203,7 @@ It was nice talking to you, goodbye!
鞚挫爠鞚� 歆€鞁� 鞝愱瞼鞐愳劀 鞛戩梾頃橁碃 甑槃頃╇媹雼�. 旃滉惮鞐愱矊 氪囲潉 韰岇姢韸疙暕雼堧嫟. 攴鸽摛鞚� 靻嶌澕 靾� 鞛堧倶鞖�? 膦€ 雿� '氙快潉 靾�'鞛堦矊 氪囲潉 毵岆摛 靾� 鞛堧倶鞖�?
-## [臧曥潣 頉� 韤挫](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/34/)
+## [臧曥潣 頉� 韤挫](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/34/)
## 瓴€韱� & 鞛愱赴欤茧弰 頃欖姷
diff --git a/6-NLP/2-Tasks/translations/README.pt-br.md b/6-NLP/2-Tasks/translations/README.pt-br.md
index 6df96eff..0aa63090 100644
--- a/6-NLP/2-Tasks/translations/README.pt-br.md
+++ b/6-NLP/2-Tasks/translations/README.pt-br.md
@@ -2,7 +2,7 @@
Para a maioria das tarefas de *processamento de linguagem natural*, o texto a ser processado precisa ser quebrado em partes e examinado, e os resultados precisam ser guardados ou cruzados com regras e data sets. Estas tarefas permitem que o programador obtenha _significado_, _intencionalidade_ ou a _frequ锚ncia_ de termos e palavras em um texto.
-## [Teste pr茅-aula](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/33?loc=ptbr)
+## [Teste pr茅-aula](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/33?loc=ptbr)
Vamos descobrir t茅cnicas frequentemente usadas no processamento de texto. Combinadas com aprendizado de m谩quina, estas t茅cnicas ajudam voc锚 a analisar grandes quantidades de texto com efici锚ncia. Contudo, antes de aplicar o aprendizado de m谩quina para estas tarefas, vamos entender os problemas enfrentados por um especialista de PLN (ou NLP).
@@ -209,7 +209,7 @@ Uma poss铆vel resposta para a tarefa est谩 [aqui](../solution/bot.py)
Implemente o bot discutido acima da se莽茫o checagem de conhecimento e teste-o em amigos. O bot consegue engan谩-los? Voc锚 consegue fazer seu bot mais convincente?
-## [Teste p贸s-aula](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/34?loc=ptbr)
+## [Teste p贸s-aula](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/34?loc=ptbr)
## Revis茫o & Autoestudo
diff --git a/6-NLP/3-Translation-Sentiment/README.md b/6-NLP/3-Translation-Sentiment/README.md
index 5415b774..1ac39530 100644
--- a/6-NLP/3-Translation-Sentiment/README.md
+++ b/6-NLP/3-Translation-Sentiment/README.md
@@ -2,7 +2,7 @@
In the previous lessons you learned how to build a basic bot using `TextBlob`, a library that embeds ML behind-the-scenes to perform basic NLP tasks such as noun phrase extraction. Another important challenge in computational linguistics is accurate _translation_ of a sentence from one spoken or written language to another.
-## [Pre-lecture quiz](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/35/)
+## [Pre-lecture quiz](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/35/)
Translation is a very hard problem compounded by the fact that there are thousands of languages and each can have very different grammar rules. One approach is to convert the formal grammar rules for one language, such as English, into a non-language dependent structure, and then translate it by converting back to another language. This approach means that you would take the following steps:
@@ -176,7 +176,7 @@ Here is a sample [solution](solution/notebook.ipynb).
Can you make Marvin even better by extracting other features from the user input?
-## [Post-lecture quiz](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/36/)
+## [Post-lecture quiz](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/36/)
## Review & Self Study
diff --git a/6-NLP/3-Translation-Sentiment/translations/README.es.md b/6-NLP/3-Translation-Sentiment/translations/README.es.md
index c86d9bef..a312a51c 100644
--- a/6-NLP/3-Translation-Sentiment/translations/README.es.md
+++ b/6-NLP/3-Translation-Sentiment/translations/README.es.md
@@ -2,7 +2,7 @@
En las lecciones anteriores aprendiste c贸mo construir un bot b谩sico usando `TextBlob`, una biblioteca que embebe aprendizaje autom谩tico tras bambalinas para realizar tareas b谩sicas de procesamiento del lenguaje natural (NLP) tales como extracci贸n de frases nominales. Otro desaf铆o importante en la ling眉铆stica computacional es la _traducci贸n_ precisa de una oraci贸n de un idioma hablado o escrito a otro.
-## [Examen previo a la lecci贸n](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/35?loc=es)
+## [Examen previo a la lecci贸n](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/35?loc=es)
La traducci贸n es siempre un problema dif铆cil compuesto por el hecho que existen miles de idiomas y cada uno puede tener distintas reglas gramaticales. Un enfoque es convertir las reglas gramaticales formales para un idioma, como el Ingl茅s, a una estructura no dependiente del idioma, y luego traducirlo al convertirlo de nuevo a otro idioma. Este enfoque significa que deber铆as realizar los siguientes pasos:
@@ -176,7 +176,7 @@ Aqu铆 tienes una [soluci贸n de muestra](../solution/notebook.ipynb).
驴Puedes hacer a Marvin a煤n mejor al extraer otras caracter铆sticas de la entrada del usuario?
-## [Examen posterior a la lecci贸n](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/36?loc=es)
+## [Examen posterior a la lecci贸n](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/36?loc=es)
## Revisi贸n y autoestudio
diff --git a/6-NLP/3-Translation-Sentiment/translations/README.it.md b/6-NLP/3-Translation-Sentiment/translations/README.it.md
index 7f82d766..77a22410 100644
--- a/6-NLP/3-Translation-Sentiment/translations/README.it.md
+++ b/6-NLP/3-Translation-Sentiment/translations/README.it.md
@@ -2,7 +2,7 @@
Nelle lezioni precedenti si 猫 imparato come creare un bot di base utilizzando `TextBlob`, una libreria che incorpora machine learning dietro le quinte per eseguire attivit脿 di base di NPL come l'estrazione di frasi nominali. Un'altra sfida importante nella linguistica computazionale 猫 _la traduzione_ accurata di una frase da una lingua parlata o scritta a un'altra.
-## [Quiz pre-lezione](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/35/?loc=it)
+## [Quiz pre-lezione](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/35/?loc=it)
La traduzione 猫 un problema molto difficile, aggravato dal fatto che ci sono migliaia di lingue e ognuna pu貌 avere regole grammaticali molto diverse. Un approccio consiste nel convertire le regole grammaticali formali per una lingua, come l'inglese, in una struttura non dipendente dalla lingua e quindi tradurla convertendola in un'altra lingua. Questo approccio significa che si dovrebbero eseguire i seguenti passaggi:
@@ -176,7 +176,7 @@ Ecco una [soluzione](../solution/notebook.ipynb) di esempio.
Si pu貌 rendere Marvin ancora migliore estraendo altre funzionalit脿 dall'input dell'utente?
-## [Quiz post-lezione](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/36/?loc=it)
+## [Quiz post-lezione](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/36/?loc=it)
## Revisione e Auto Apprendimento
diff --git a/6-NLP/3-Translation-Sentiment/translations/README.ko.md b/6-NLP/3-Translation-Sentiment/translations/README.ko.md
index a18dee6e..e1828009 100644
--- a/6-NLP/3-Translation-Sentiment/translations/README.ko.md
+++ b/6-NLP/3-Translation-Sentiment/translations/README.ko.md
@@ -2,7 +2,7 @@
鞚挫爠 臧曥潣鞐愳劀 noun phrase 於旍稖頃橂姅 旮办磮 NLP 鞛戩梾鞚� 頃橁赴 鞙勴暣 ML behind-the-scenes鞚� 韽暔頃� 霛检澊敫岆煬毽澑, `TextBlob`鞙茧 旮半掣鞝侅澑 氪囲潉 毵岆摐電� 氚╈嫕鞚� 氚办洜鞀惦媹雼�. 旎错摠韯� 鞏胳柎頃欖棎靹� 雼るジ 欷戩殧頃� 霃勳爠鞚€ 甑憪雮� 雼るジ 鞏胳柎搿� 氍胳灔鞚� 鞝曧檿頃橁矊 _translation_ 頃橂姅 瓴冹瀰雼堧嫟.
-## [臧曥潣 鞝� 韤挫](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/35/)
+## [臧曥潣 鞝� 韤挫](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/35/)
氩堨棴鞚€ 觳滌棳 臧� 鞏胳柎鞕€ 臧侅瀽 毵庫澊 雼るジ 氍鸽矔 攴滌箼鞚� 鞛堧嫟電� 靷嫟鞐� 鞚橅暣靹� 頃╈硱歆� 毵れ毎 鞏措牑鞖� 氍胳牅鞛呺媹雼�. 頃� 鞝戧芳 氚╈嫕鞚€ 鞓侅柎觳橂熂, 頃� 鞏胳柎鞚� 順曥嫕鞝侅澑 氍鸽矔 攴滌箼鞚� 牍�-鞏胳柎 膦呾啀 甑“搿� 氤€頇橅晿瓿�, 雼るジ 鞏胳柎搿� 氤€頇橅晿氅挫劀 氩堨棴頃╇媹雼�. 鞚� 鞝戧芳 氚╈嫕鞚€ 雼れ潓 雼硠搿� 歆勴枆霅滊嫟電� 鞝愳潉 鞚橂頃╇媹雼�:
@@ -177,7 +177,7 @@ Darcy, as well as Elizabeth, really loved them; and they were
靷毄鞛� 鞛呺牓鞙茧 雼るジ features毳� 於旍稖頃挫劀 Marvin鞚� 雿� 膦嬯矊 毵岆摛 靾� 鞛堧倶鞖�?
-## [臧曥潣 頉� 韤挫](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/36/)
+## [臧曥潣 頉� 韤挫](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/36/)
## 瓴€韱� & 鞛愱赴欤茧弰 頃欖姷
diff --git a/6-NLP/4-Hotel-Reviews-1/README.md b/6-NLP/4-Hotel-Reviews-1/README.md
index 77a9537e..151745aa 100644
--- a/6-NLP/4-Hotel-Reviews-1/README.md
+++ b/6-NLP/4-Hotel-Reviews-1/README.md
@@ -6,7 +6,7 @@ In this section you will use the techniques in the previous lessons to do some e
- how to calculate some new data based on the existing columns
- how to save the resulting dataset for use in the final challenge
-## [Pre-lecture quiz](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/37/)
+## [Pre-lecture quiz](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/37/)
### Introduction
@@ -393,7 +393,7 @@ Now that you have explored the dataset, in the next lesson you will filter the d
This lesson demonstrates, as we saw in previous lessons, how critically important it is to understand your data and its foibles before performing operations on it. Text-based data, in particular, bears careful scrutiny. Dig through various text-heavy datasets and see if you can discover areas that could introduce bias or skewed sentiment into a model.
-## [Post-lecture quiz](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/38/)
+## [Post-lecture quiz](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/38/)
## Review & Self Study
diff --git a/6-NLP/4-Hotel-Reviews-1/translations/README.es.md b/6-NLP/4-Hotel-Reviews-1/translations/README.es.md
index 8c478465..81b419b2 100644
--- a/6-NLP/4-Hotel-Reviews-1/translations/README.es.md
+++ b/6-NLP/4-Hotel-Reviews-1/translations/README.es.md
@@ -6,7 +6,7 @@ En esta secci贸n usar谩s las t茅cnicas de las lecciones anteriores para hacer un
- c贸mo calcular algunos datos nuevos bas谩ndote en las columnas existentes
- c贸mo guardar el conjunto de datos resultante para usarlo en el desaf铆o final
-## [Examen previo a la lecci贸n](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/37?loc=es)
+## [Examen previo a la lecci贸n](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/37?loc=es)
### Introducci贸n
@@ -404,7 +404,7 @@ Ahora que has explorado el conjunto de datos, en la pr贸xima lecci贸n filtrar谩s
Esta lecci贸n demuestra, como vimos en lecciones anteriores, qu茅 tan cr铆ticamente importante es entender tus datos y sus imperfecciones antes de realizar operaciones sobre ellos. Los datos basados en texto, requieren particularmente un minucioso escrutinio. Profundiza en grandes conjuntos de datos basados en texto y ve si puedes descubrir 谩reas que podr铆an presentar sesgos o sentimientos sesgados en un modelo.
-## [Examen posterior a la lecci贸n](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/38?loc=es)
+## [Examen posterior a la lecci贸n](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/38?loc=es)
## Revisi贸n y autoestudio
diff --git a/6-NLP/4-Hotel-Reviews-1/translations/README.it.md b/6-NLP/4-Hotel-Reviews-1/translations/README.it.md
index 47c36eb3..ff29d1b7 100644
--- a/6-NLP/4-Hotel-Reviews-1/translations/README.it.md
+++ b/6-NLP/4-Hotel-Reviews-1/translations/README.it.md
@@ -6,7 +6,7 @@ In questa sezione si utilizzeranno le tecniche delle lezioni precedenti per eseg
- come calcolare alcuni nuovi dati in base alle colonne esistenti
- come salvare l'insieme di dati risultante per l'uso nella sfida finale
-## [Quiz pre-lezione](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/37/?loc=it)
+## [Quiz pre-lezione](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/37/?loc=it)
### Introduzione
@@ -401,7 +401,7 @@ Ora che si 猫 esplorato l'insieme di dati, nella prossima lezione si filtreranno
Questa lezione dimostra, come visto nelle lezioni precedenti, quanto sia di fondamentale importanza comprendere i dati e le loro debolezze prima di eseguire operazioni su di essi. I dati basati su testo, in particolare, sono oggetto di un attento esame. Esaminare vari insiemi di dati contenenti principalmente testo e vedere se si riesce a scoprire aree che potrebbero introdurre pregiudizi o sentiment distorti in un modello.
-## [Quiz post-lezione](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/38/?loc=it)
+## [Quiz post-lezione](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/38/?loc=it)
## Revisione e Auto Apprendimento
diff --git a/6-NLP/4-Hotel-Reviews-1/translations/README.ko.md b/6-NLP/4-Hotel-Reviews-1/translations/README.ko.md
index f2b2852f..78bb9fa6 100644
--- a/6-NLP/4-Hotel-Reviews-1/translations/README.ko.md
+++ b/6-NLP/4-Hotel-Reviews-1/translations/README.ko.md
@@ -6,7 +6,7 @@
- 鞚措 臁挫灛頃橂姅 鞐挫潉 旮半皹鞙茧 鞚茧秬 靸堧鞖� 雿办澊韯半ゼ 瓿勳偘頃橂姅 氚╈嫕
- 斓滌 霃勳爠鞐愳劀 靷毄頃橁碃鞛� 瓴瓣臣 雿办澊韯办厠鞚� 鞝€鞛ロ晿電� 氚╈嫕
-## [臧曥潣 鞝� 韤挫](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/37/)
+## [臧曥潣 鞝� 韤挫](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/37/)
### 靻岅皽
@@ -397,7 +397,7 @@ print("Loading took " + str(round(end - start, 2)) + " seconds")
鞚挫爠 臧曥潣鞐愳劀 氤� 瓴冹矘霟�, 鞚� 臧曥潣鞐愳劀 鞛戩梾頃橁赴 鞝� 雿办澊韯办檧 鞎届爯鞚� 鞚错暣頃橂姅 瓴冹澊 鞏茧雮� 旃橂獏鞝侅澊瓴� 欷戩殧頃滌 氤挫棳欷嶋媹雼�. 韸闺硠頌�, 韰嶌姢韸�-旮半皹 雿办澊韯半姅, 臁办嫭頌� 臁办偓頃挫暭 頃╇媹雼�. 雼れ枒頃� text-heavy 雿办澊韯办厠鞚� 韺岆炒瓿� 氇嵏鞐愳劀 旃橃毎旃橁卑雮� 韼疙枼霅� 臧愳爼鞙茧 雭检泴雴撿潃 鞓侅棴鞚� 彀眷潉 靾� 鞛堧姅歆€ 頇曥澑頃╇媹雼�.
-## [臧曥潣 頉� 韤挫](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/38/)
+## [臧曥潣 頉� 韤挫](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/38/)
## 瓴€韱� & 鞛愱赴欤茧弰 頃欖姷
diff --git a/6-NLP/5-Hotel-Reviews-2/README.md b/6-NLP/5-Hotel-Reviews-2/README.md
index 092ff88a..c20d3340 100644
--- a/6-NLP/5-Hotel-Reviews-2/README.md
+++ b/6-NLP/5-Hotel-Reviews-2/README.md
@@ -1,7 +1,7 @@
# Sentiment analysis with hotel reviews
Now that you have explored the dataset in detail, it's time to filter the columns and then use NLP techniques on the dataset to gain new insights about the hotels.
-## [Pre-lecture quiz](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/39/)
+## [Pre-lecture quiz](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/39/)
### Filtering & Sentiment Analysis Operations
@@ -360,7 +360,7 @@ To review, the steps are:
When you started, you had a dataset with columns and data but not all of it could be verified or used. You've explored the data, filtered out what you don't need, converted tags into something useful, calculated your own averages, added some sentiment columns and hopefully, learned some interesting things about processing natural text.
-## [Post-lecture quiz](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/40/)
+## [Post-lecture quiz](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/40/)
## Challenge
diff --git a/6-NLP/5-Hotel-Reviews-2/translations/README.es.md b/6-NLP/5-Hotel-Reviews-2/translations/README.es.md
index 28e35d30..00822c68 100644
--- a/6-NLP/5-Hotel-Reviews-2/translations/README.es.md
+++ b/6-NLP/5-Hotel-Reviews-2/translations/README.es.md
@@ -2,7 +2,7 @@
Ahora que has explorado a detalle el conjunto de datos, es momento de filtrar las columnas y luego usar t茅cnicas de procesamiento del lenguaje natural sobre el conjunto de datos para obtener nuevos conocimientos acerca de los hoteles.
-## [Examen previo a la lecci贸n](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/39?loc=es)
+## [Examen previo a la lecci贸n](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/39?loc=es)
### Filtrado y operaciones de an谩lisis de sentimiento
@@ -361,7 +361,7 @@ Para revisar, los pasos son:
Cuando iniciaste, ten铆as un conjunto de datos con columnas y datos pero no todos ello pod铆an ser verificados o usados. Exploraste los datos, filtraste lo que no necesitas, convertiste etiquetas en algo 煤til, calculaste tus propios promedios, agregaste algunas columnas de sentimiento y espero hayas aprendido cosas interesantes acerca de procesar texto natural.
-## [Examen posterior a la lecci贸n](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/40?loc=es)
+## [Examen posterior a la lecci贸n](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/40?loc=es)
## Desaf铆o
diff --git a/6-NLP/5-Hotel-Reviews-2/translations/README.it.md b/6-NLP/5-Hotel-Reviews-2/translations/README.it.md
index 6e62cd9a..c9a9fa00 100644
--- a/6-NLP/5-Hotel-Reviews-2/translations/README.it.md
+++ b/6-NLP/5-Hotel-Reviews-2/translations/README.it.md
@@ -2,7 +2,7 @@
Ora che si 猫 esplorato in dettaglio l'insieme di dati, 猫 il momento di filtrare le colonne e quindi utilizzare le tecniche NLP sull'insieme di dati per ottenere nuove informazioni sugli hotel.
-## [Quiz pre-lezione](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/39/?loc=it)
+## [Quiz pre-lezione](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/39/?loc=it)
### Operazioni di Filtraggio e Analisi del Sentiment
@@ -361,7 +361,7 @@ Per riepilogare, i passaggi sono:
Quando si 猫 iniziato, si disponeva di un insieme di dati con colonne e dati, ma non tutto poteva essere verificato o utilizzato. Si sono esplorati i dati, filtrato ci貌 che non serve, convertito i tag in qualcosa di utile, calcolato le proprie medie, aggiunto alcune colonne di sentiment e, si spera, imparato alcune cose interessanti sull'elaborazione del testo naturale.
-## [Quiz post-lezione](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/40/?loc=it)
+## [Quiz post-lezione](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/40/?loc=it)
## Sfida
diff --git a/6-NLP/5-Hotel-Reviews-2/translations/README.ko.md b/6-NLP/5-Hotel-Reviews-2/translations/README.ko.md
index aeaa0b2d..22a1eb4f 100644
--- a/6-NLP/5-Hotel-Reviews-2/translations/README.ko.md
+++ b/6-NLP/5-Hotel-Reviews-2/translations/README.ko.md
@@ -2,7 +2,7 @@
歆€旮堦箤歆€ 鞛愳劯頌� 雿办澊韯办厠鞚� 靷错幋氤挫晿鞙茧┌, 鞐挫潉 頃勴劙毵來晿瓿� 雿办澊韯办厠鞙茧 NLP 旮办垹鞚� 靷毄頃橃棳 順疙厰鞐� 雽€頃� 靸堧鞖� 鞁滉皝鞚� 鞏魂矊 霅� 鞁滉皠鞛呺媹雼�.
-## [臧曥潣 鞝� 韤挫](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/39/)
+## [臧曥潣 鞝� 韤挫](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/39/)
### 頃勴劙毵� & 臧愳爼 攵勳劃 鞛戩梾
@@ -361,7 +361,7 @@ df.to_csv(r"../data/Hotel_Reviews_NLP.csv", index = False)
鞁滌瀾頄堨潉 霑�, 鞐搓臣 雿办澊韯半 鞚措（鞏挫 雿办澊韯办厠鞚� 鞐堨棃歆€毵� 氇憪 雼� 頇曥澑霅橁卑雮� 靷毄霅橃 鞎婌晿鞀惦媹雼�. 雿办澊韯半ゼ 靷错幋氤挫晿鞙茧┌, 頃勳殧鞐嗠姅 瓴冹潃 頃勴劙毵來暣靹� 歆€鞗犼碃, 鞙犾毄頃橁矊 韮滉犯毳� 氤€頇橅枅瓿�, 韽夑窢鞚� 瓿勳偘頄堨溂氅�, 鞚茧秬 臧愳爼 鞐挫潉 於旉皜頃橁碃 旮半寑頃橂┐靹�, 鞛愳棸鞏� 觳橂Μ鞐� 雽€頃� 鞚茧秬 頋ル搿滌毚 靷嫟鞚� 頃欖姷頄堨姷雼堧嫟.
-## [臧曥潣 頉� 韤挫](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/40/)
+## [臧曥潣 頉� 韤挫](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/40/)
## 霃勳爠
diff --git a/7-TimeSeries/1-Introduction/README.md b/7-TimeSeries/1-Introduction/README.md
index e875fdc9..66af0a20 100644
--- a/7-TimeSeries/1-Introduction/README.md
+++ b/7-TimeSeries/1-Introduction/README.md
@@ -10,7 +10,7 @@ In this lesson and the following one, you will learn a bit about time series for
> 馃帴 Click the image above for a video about time series forecasting
-## [Pre-lecture quiz](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/41/)
+## [Pre-lecture quiz](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/41/)
It's a useful and interesting field with real value to business, given its direct application to problems of pricing, inventory, and supply chain issues. While deep learning techniques have started to be used to gain more insights to better predict future performance, time series forecasting remains a field greatly informed by classic ML techniques.
@@ -174,7 +174,7 @@ In the next lesson, you will create an ARIMA model to create some forecasts.
Make a list of all the industries and areas of inquiry you can think of that would benefit from time series forecasting. Can you think of an application of these techniques in the arts? In Econometrics? Ecology? Retail? Industry? Finance? Where else?
-## [Post-lecture quiz](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/42/)
+## [Post-lecture quiz](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/42/)
## Review & Self Study
diff --git a/7-TimeSeries/1-Introduction/translations/README.es.md b/7-TimeSeries/1-Introduction/translations/README.es.md
index 04a88a99..d9402560 100644
--- a/7-TimeSeries/1-Introduction/translations/README.es.md
+++ b/7-TimeSeries/1-Introduction/translations/README.es.md
@@ -10,7 +10,7 @@ En esta lecci贸n y la siguiente, aprender谩s un poco acerca de la predicci贸n de
> 馃帴 Da clic en la imagen de arriba para ver un video acerca de la predicci贸n de series de tiempo
-## [Examen previo a la lecci贸n](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/41?loc=es)
+## [Examen previo a la lecci贸n](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/41?loc=es)
Es un campo 煤til e interesante con valor real para el negocio, dada su aplicaci贸n directa a problemas de precio, inventario e incidentes de cadenas de suministro. Mientras que las t茅cnicas de aprendizaje profundo han comenzado a usarse para ganar m谩s conocimiento para mejorar el rendimiento de futuras predicciones, la predicci贸n de series de tiempo sigue siendo un campo muy informado por t茅cnicas de aprendizaje autom谩tico cl谩sico.
@@ -175,7 +175,7 @@ En la siguiente lecci贸n, crear谩s un modelo ARIMA para realizar algunas predicc
Haz una lista de todas las industrias y 谩reas de consulta en las que puedes pensar que se beneficiar铆an de la predicci贸n de series de tiempo. 驴Puedes pensar en una aplicaci贸n de estas t茅cnicas en las artes, en la econometr铆a, ecolog铆a, venta al menudeo, la industria, finanzas? 驴D贸nde m谩s?
-## [Examen posterior a la lecci贸n](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/42?loc=es)
+## [Examen posterior a la lecci贸n](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/42?loc=es)
## Revisi贸n y autoestudio
diff --git a/7-TimeSeries/1-Introduction/translations/README.it.md b/7-TimeSeries/1-Introduction/translations/README.it.md
index 5d4f49a4..2c3ee610 100644
--- a/7-TimeSeries/1-Introduction/translations/README.it.md
+++ b/7-TimeSeries/1-Introduction/translations/README.it.md
@@ -10,7 +10,7 @@ In questa lezione e nella successiva si imparer脿 qualcosa sulla previsione dell
> 馃帴 Fare clic sull'immagine sopra per un video sulla previsione delle serie temporali
-## [Quiz pre-lezione](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/41/?loc=it)
+## [Quiz pre-lezione](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/41/?loc=it)
脠 un campo utile e interessante con un valore reale per il business, data la sua applicazione diretta a problemi di prezzi, inventario e problemi della catena di approvvigionamento. Mentre le tecniche di deep learning hanno iniziato a essere utilizzate per acquisire maggiori informazioni per prevedere meglio le prestazioni future, la previsione delle serie temporali rimane un campo ampiamente informato dalle tecniche classiche di ML.
@@ -174,7 +174,7 @@ Nella prossima lezione, si creer脿 un modello ARIMA per creare alcune previsioni
Fare un elenco di tutti i settori e le aree di indagine che vengono in mente che potrebbero trarre vantaggio dalla previsione delle serie temporali. Si riesce a pensare a un'applicazione di queste tecniche nelle arti? In Econometria? Ecologia? Vendita al Dettaglio? Industria? Finanza? Dove se no?
-## [Quiz post-lezione](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/42/?loc=it)
+## [Quiz post-lezione](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/42/?loc=it)
## Revisione e Auto Apprendimento
diff --git a/7-TimeSeries/1-Introduction/translations/README.ko.md b/7-TimeSeries/1-Introduction/translations/README.ko.md
index a096a6e2..d949913b 100644
--- a/7-TimeSeries/1-Introduction/translations/README.ko.md
+++ b/7-TimeSeries/1-Introduction/translations/README.ko.md
@@ -10,7 +10,7 @@
> 馃帴 鞚措歆€毳� 雸岆煬靹� time series forecasting鞐� 雽€頃� 牍勲敂鞓るゼ 氪呺媹雼�
-## [臧曥潣 鞝� 韤挫](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/41/)
+## [臧曥潣 鞝� 韤挫](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/41/)
臧€瓴�, 鞛碃, 攴鸽Μ瓿� 瓿店笁瓿� 鞐瓣磤霅� 鞚挫妶鞐� 歆侅爲 鞝侅毄頃橁矊 霅滊嫟氅�, 牍勳雼堨姢鞐� 鞁れ牅搿� 臧€旃橃瀳電� 鞙犾毄頃橁碃 頋ル搿滌毚 頃勲摐臧€ 霅╇媹雼�. 霐ル煬雼� 旮办垹鞚€ 氙鸽灅鞚� 靹彪姤鞚� 鞛� 鞓堨浮頃橁赴 鞙勴暣 雿� 毵庫潃 鞚胳偓鞚错姼毳� 鞏魂碃鞛� 靷毄頄堨毵�, time series forecasting鞚€ classic ML 旮办垹鞐愳劀 歆€靻嶌爜鞙茧 毵庫潃 鞝曤炒毳� 鞏浑姅 頃勲摐鞛呺媹雼�.
@@ -175,7 +175,7 @@ seasonality鞚� 霃呺鞝侅溂搿�, 1雲� 氤措嫟 旮� 瓴届牅 旃泊臧欖潃 long-run cyc
time series forecasting鞐愳劀 鞏混潉 靾� 鞛堧嫟瓿� 靸濌皝頃� 靾� 鞛堧姅 氇摖 靷办梾瓿� 臁办偓 鞓侅棴鞚� 毽姢韸鸽ゼ 毵岆摥雼堧嫟. 鞓堨垹鞐� 鞚� 旮办垹鞚� 鞝侅毄頃� 靾� 鞛堧嫟瓿� 靸濌皝頃橂倶鞖�? 瓴届牅頃欖棎靹�? 靸濏儨頃欖棎靹�? 毽厡鞚检棎靹�? 靷办梾鞐愳劀? 旮堨湹鞐愳劀? 霕� 雼るジ 瓿踌潃 鞏措敇臧€鞖�?
-## [臧曥潣 頉� 韤挫](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/42/)
+## [臧曥潣 頉� 韤挫](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/42/)
## 瓴€韱� & 鞛愱赴欤茧弰 頃欖姷
diff --git a/7-TimeSeries/2-ARIMA/README.md b/7-TimeSeries/2-ARIMA/README.md
index b421a446..8e2e775e 100644
--- a/7-TimeSeries/2-ARIMA/README.md
+++ b/7-TimeSeries/2-ARIMA/README.md
@@ -6,7 +6,7 @@ In the previous lesson, you learned a bit about time series forecasting and load
> 馃帴 Click the image above for a video: A brief introduction to ARIMA models. The example is done in R, but the concepts are universal.
-## [Pre-lecture quiz](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/43/)
+## [Pre-lecture quiz](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/43/)
## Introduction
@@ -383,7 +383,7 @@ Check the accuracy of your model by testing its mean absolute percentage error (
Dig into the ways to test the accuracy of a Time Series Model. We touch on MAPE in this lesson, but are there other methods you could use? Research them and annotate them. A helpful document can be found [here](https://otexts.com/fpp2/accuracy.html)
-## [Post-lecture quiz](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/44/)
+## [Post-lecture quiz](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/44/)
## Review & Self Study
diff --git a/7-TimeSeries/2-ARIMA/translations/README.it.md b/7-TimeSeries/2-ARIMA/translations/README.it.md
index 6100b0bc..9db7cd80 100644
--- a/7-TimeSeries/2-ARIMA/translations/README.it.md
+++ b/7-TimeSeries/2-ARIMA/translations/README.it.md
@@ -6,7 +6,7 @@ Nella lezione precedente, si 猫 imparato qualcosa sulla previsione delle serie t
> 馃帴 Fare clic sull'immagine sopra per un video: Una breve introduzione ai modelli ARIMA. L'esempio 猫 fatto in linguaggio R, ma i concetti sono universali.
-## [Quiz pre-lezione](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/43/?loc=it)
+## [Quiz pre-lezione](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/43/?loc=it)
## Introduzione
@@ -383,7 +383,7 @@ Controllare l'accuratezza del modello testando il suo errore percentuale medio a
Scoprire i modi per testare l'accuratezza di un modello di serie temporali. Si esamina MAPE in questa lezione, ma ci sono altri metodi che si potrebbero usare? Ricercarli e annotarli. Un documento utile pu貌 essere trovato [qui](https://otexts.com/fpp2/accuracy.html)
-## [Quiz post-lezione](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/44/?loc=it)
+## [Quiz post-lezione](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/44/?loc=it)
## Revisione e Auto Apprendimento
diff --git a/7-TimeSeries/2-ARIMA/translations/README.ko.md b/7-TimeSeries/2-ARIMA/translations/README.ko.md
index 030d418d..7e04a53c 100644
--- a/7-TimeSeries/2-ARIMA/translations/README.ko.md
+++ b/7-TimeSeries/2-ARIMA/translations/README.ko.md
@@ -6,7 +6,7 @@
> 馃帴 鞓侅儊鞚� 氤措牑氅� 鞚措歆€ 韥措Ν: A brief introduction to ARIMA models. The example is done in R, but the concepts are universal.
-## [臧曥潣 鞝� 韤挫](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/43/)
+## [臧曥潣 鞝� 韤挫](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/43/)
## 靻岅皽
@@ -383,7 +383,7 @@ Walk-forward 瓴€靷姅 time series 氇嵏 韽夑皜鞚� 斓滌爜 響滌鞚搓碃 鞚� 頂�
Time Series 氇嵏鞚� 鞝曧檿霃勲ゼ 韰岇姢韸疙暊 氚╈嫕鞚� 韺岆磪雼堧嫟. 鞚� 臧曥潣鞐愳劀 MAPE鞚� 雼る（歆€毵�, 靷毄頃� 雼るジ 氚╈嫕鞚� 鞛堧倶鞖�? 臁办偓頃措炒瓿� 觳柛頃措磪雼堧嫟. 霃勳泙鞚� 氚涭潉 靾� 鞛堧姅 氍胳劀電� [here](https://otexts.com/fpp2/accuracy.html)鞐愳劀 彀眷潉 靾� 鞛堨姷雼堧嫟.
-## [臧曥潣 頉� 韤挫](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/44/)
+## [臧曥潣 頉� 韤挫](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/44/)
## 瓴€韱� & 鞛愱赴欤茧弰 頃欖姷
diff --git a/7-TimeSeries/3-SVR/README.md b/7-TimeSeries/3-SVR/README.md
index 5fcb8719..cf500558 100644
--- a/7-TimeSeries/3-SVR/README.md
+++ b/7-TimeSeries/3-SVR/README.md
@@ -2,7 +2,7 @@
In the previous lesson, you learned how to use ARIMA model to make time series predictions. Now you'll be looking at Support Vector Regressor model which is a regressor model used to predict continuous data.
-## [Pre-lecture quiz](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/51/)
+## [Pre-lecture quiz](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/51/)
## Introduction
@@ -367,7 +367,7 @@ MAPE: 2.0572089029888656 %
- Try to use different kernel functions for the model and analyze their performances on the dataset. A helpful document can be found [here](https://scikit-learn.org/stable/modules/svm.html#kernel-functions).
- Try using different values for `timesteps` for the model to look back to make prediction.
-## [Post-lecture quiz](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/52/)
+## [Post-lecture quiz](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/52/)
## Review & Self Study
diff --git a/8-Reinforcement/1-QLearning/README.md b/8-Reinforcement/1-QLearning/README.md
index 2e207429..c7d6263e 100644
--- a/8-Reinforcement/1-QLearning/README.md
+++ b/8-Reinforcement/1-QLearning/README.md
@@ -11,7 +11,7 @@ By using reinforcement learning and a simulator (the game), you can learn how to
> 馃帴 Click the image above to hear Dmitry discuss Reinforcement Learning
-## [Pre-lecture quiz](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/45/)
+## [Pre-lecture quiz](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/45/)
## Prerequisites and Setup
@@ -314,6 +314,6 @@ The learnings can be summarized as:
Overall, it is important to remember that the success and quality of the learning process significantly depends on parameters, such as learning rate, learning rate decay, and discount factor. Those are often called **hyperparameters**, to distinguish them from **parameters**, which we optimize during training (for example, Q-Table coefficients). The process of finding the best hyperparameter values is called **hyperparameter optimization**, and it deserves a separate topic.
-## [Post-lecture quiz](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/46/)
+## [Post-lecture quiz](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/46/)
## Assignment [A More Realistic World](assignment.md)
diff --git a/8-Reinforcement/1-QLearning/translations/README.it.md b/8-Reinforcement/1-QLearning/translations/README.it.md
index a0576ea2..613e2aa0 100644
--- a/8-Reinforcement/1-QLearning/translations/README.it.md
+++ b/8-Reinforcement/1-QLearning/translations/README.it.md
@@ -11,7 +11,7 @@ Usando reinforcement learning e un simulatore (il gioco), si pu貌 imparare a gio
> 馃帴 Fare clic sull'immagine sopra per ascoltare Dmitry discutere sul reinforcement learning
-## [Quiz pre-lezione](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/45/?loc=it)
+## [Quiz pre-lezione](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/45/?loc=it)
## Prerequisiti e Configurazione
@@ -315,6 +315,6 @@ Gli apprendimenti possono essere riassunti come:
Nel complesso, 猫 importante ricordare che il successo e la qualit脿 del processo di apprendimento dipendono in modo significativo da parametri come il tasso di apprendimento, il decadimento del tasso di apprendimento e il fattore di sconto. Questi sono spesso chiamati **iperparametri**, per distinguerli dai **parametri**, che si ottimizzano durante l'allenamento (ad esempio, i coefficienti della Q-Table). Il processo per trovare i valori migliori degli iperparametri 猫 chiamato **ottimizzazione degli iperparametri** e merita un argomento a parte.
-## [Quiz post-lezione](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/46/?loc=fr)
+## [Quiz post-lezione](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/46/?loc=fr)
## Incarico: [Un mondo pi霉 realistico](assignment.it.md)
diff --git a/8-Reinforcement/1-QLearning/translations/README.ko.md b/8-Reinforcement/1-QLearning/translations/README.ko.md
index b990ed5a..86cca554 100644
--- a/8-Reinforcement/1-QLearning/translations/README.ko.md
+++ b/8-Reinforcement/1-QLearning/translations/README.ko.md
@@ -11,7 +11,7 @@ reinforcement learning瓿� (瓴岇瀯) 鞁滊霠堨澊韯半, 靷挫晞雮碃 臧€電ロ暅
> 馃帴 Dmitry discuss Reinforcement Learning 霌れ溂霠る┐ 鞚措歆€ 韥措Ν
-## [臧曥潣 鞝� 韤挫](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/45/)
+## [臧曥潣 鞝� 韤挫](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/45/)
## 鞝勳牅臁瓣贝 氚� 靹れ爼
@@ -315,7 +315,7 @@ print_statistics(qpolicy)
鞝勳泊鞝侅溂搿�, 頃欖姷 頂勲靹胳姢鞚� 靹标车瓿� 韤勲Μ韹半姅 頃欖姷毳�, 頃欖姷毳� 臧愳唽, 攴鸽Μ瓿� 臧愱皜鞙矘霟� 韺岆澕氙疙劙鞐� 旮半皹頃橂姅瓴� 靸侂嫻頌� 欷戩殧頃橂嫟電� 鞝愳潉 旮办柕頃╇媹雼�. 頉堧牗頃橂┐靹� 斓滌爜頇旐晿氅� (鞓堨嫓搿�, Q-Table coefficients), **parameters**鞕€ 甑硠頃挫劀, 臧€雭� **hyperparameters**霛缄碃 攵堧雼堧嫟. 斓滉碃鞚� hyperparameter 臧掛潉 彀倦姅 頂勲靹胳姢電� **hyperparameter optimization**鞚措澕瓿� 攵堧Μ氅�, 氤勲弰鞚� 韱犿斀鞚� 鞛堨潉 毵岉暕雼堧嫟.
-## [臧曥潣 頉� 韤挫](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/46/)
+## [臧曥潣 頉� 韤挫](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/46/)
## 瓿检牅
diff --git a/8-Reinforcement/1-QLearning/translations/README.zh-cn.md b/8-Reinforcement/1-QLearning/translations/README.zh-cn.md
index fc0479ca..e047c895 100644
--- a/8-Reinforcement/1-QLearning/translations/README.zh-cn.md
+++ b/8-Reinforcement/1-QLearning/translations/README.zh-cn.md
@@ -11,7 +11,7 @@
> 馃帴 鐐瑰嚮涓婂浘瑙傜湅 Dmitry 璁ㄨ寮哄寲瀛︿範
-## [璇惧墠娴嬮獙](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/45/)
+## [璇惧墠娴嬮獙](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/45/)
## 鍏堝喅鏉′欢鍜岃缃�
@@ -315,6 +315,6 @@ print_statistics(qpolicy)
鎬荤殑鏉ヨ锛岄噸瑕佺殑鏄璁颁綇瀛︿範杩囩▼鐨勬垚鍔熷拰璐ㄩ噺鍦ㄥ緢澶х▼搴︿笂鍙栧喅浜庡弬鏁帮紝渚嬪瀛︿範鐜囥€佸涔犵巼琛板噺鍜屾姌鎵ｅ洜瀛愩€傝繖浜涢€氬父绉颁负**瓒呭弬鏁�**锛屼互鍖哄埆浜庢垜浠湪璁粌鏈熼棿浼樺寲鐨�**鍙傛暟**锛堜緥濡傦紝Q-Table 绯绘暟锛夈€傚鎵炬渶浣宠秴鍙傛暟鍊肩殑杩囩▼绉颁负**瓒呭弬鏁颁紭鍖�**锛屽畠鍊煎緱涓€涓崟鐙殑璇濋鏉ヤ粙缁嶃€�
-## [璇惧悗娴嬮獙](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/46/)
+## [璇惧悗娴嬮獙](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/46/)
## 浣滀笟[涓€涓洿鐪熷疄鐨勪笘鐣宂(assignment.zh-cn.md)
diff --git a/8-Reinforcement/2-Gym/README.md b/8-Reinforcement/2-Gym/README.md
index 7faea6e0..de3eac37 100644
--- a/8-Reinforcement/2-Gym/README.md
+++ b/8-Reinforcement/2-Gym/README.md
@@ -2,7 +2,7 @@
The problem we have been solving in the previous lesson might seem like a toy problem, not really applicable for real life scenarios. This is not the case, because many real world problems also share this scenario - including playing Chess or Go. They are similar, because we also have a board with given rules and a **discrete state**.
https://white-water-09ec41f0f.azurestaticapps.net/
-## [Pre-lecture quiz](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/47/)
+## [Pre-lecture quiz](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/47/)
## Introduction
@@ -329,7 +329,7 @@ You should see something like this:
> **Task 4**: Here we were not selecting the best action on each step, but rather sampling with corresponding probability distribution. Would it make more sense to always select the best action, with the highest Q-Table value? This can be done by using `np.argmax` function to find out the action number corresponding to highers Q-Table value. Implement this strategy and see if it improves the balancing.
-## [Post-lecture quiz](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/48/)
+## [Post-lecture quiz](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/48/)
## Assignment: [Train a Mountain Car](assignment.md)
diff --git a/8-Reinforcement/2-Gym/translations/README.it.md b/8-Reinforcement/2-Gym/translations/README.it.md
index 4ed87100..30bf4f1b 100644
--- a/8-Reinforcement/2-Gym/translations/README.it.md
+++ b/8-Reinforcement/2-Gym/translations/README.it.md
@@ -2,7 +2,7 @@
Il problema risolto nella lezione precedente potrebbe sembrare un problema giocattolo, non propriamente applicabile a scenari di vita reale. Questo non 猫 il caso, perch茅 anche molti problemi del mondo reale condividono questo scenario, incluso Scacchi o Go. Sono simili, perch茅 anche in quei casi si ha una tavolo di gioco con regole date e uno **stato discreto**.
-## [Quiz pre-lezione](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/47/?loc=it)
+## [Quiz pre-lezione](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/47/?loc=it)
## Introduzione
@@ -329,7 +329,7 @@ Si dovrebbe vedere qualcosa del genere:
> **Compito 4**: Qui non si stava selezionando l'azione migliore per ogni passaggio, ma piuttosto campionando con la corrispondente distribuzione di probabilit脿. Avrebbe pi霉 senso selezionare sempre l'azione migliore, con il valore Q-Table pi霉 alto? Questo pu貌 essere fatto usando la funzione `np.argmax` per trovare il numero dell'azione corrispondente al valore della Q-Table pi霉 alto. Implementare questa strategia e vedere se migliora il bilanciamento.
-## [Quiz post-lezione](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/48/?loc=it)
+## [Quiz post-lezione](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/48/?loc=it)
## Compito: [addestrare un'auto di montagna](assignment.it.md)
diff --git a/8-Reinforcement/2-Gym/translations/README.ko.md b/8-Reinforcement/2-Gym/translations/README.ko.md
index 0002b2be..dde04e45 100644
--- a/8-Reinforcement/2-Gym/translations/README.ko.md
+++ b/8-Reinforcement/2-Gym/translations/README.ko.md
@@ -2,7 +2,7 @@
鞚挫爠 臧曥潣鞐愳劀 頀€鞐堧崢 氍胳牅電� 鞛ル倻臧� 氍胳牅觳橂熂 氤挫澕 靾� 鞛堦碃, 鞁れ牅 鞁滊倶毽槫鞐愳劀 歆勳 鞝侅毄霅橃 鞎婌姷雼堧嫟. 觳挫姢雮� 氚旊憫鞚� 歃愱赴電� 瓴冹潉 韽暔頃� - 鞁滊倶毽槫鞐� 毵庫潃 鞁れ牅 氍胳牅鞕€ 瓿奠湢頃橁赴 霑岆鞐�, 鞚� 旒€鞚挫姢電� 鞎勲嫏雼堧嫟. 欤检柎歆� 耄瓣臣 **discrete state**毳� 氤措摐臧€ 臧€歆€瓿� 鞛堦赴 霑岆鞐� 牍勳姺頃╇媹雼�.
-## [臧曥潣 鞝� 韤挫](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/47/)
+## [臧曥潣 鞝� 韤挫](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/47/)
## 靻岅皽
@@ -329,7 +329,7 @@ env.close()
> **Task 4**: 鞐赴鞐愲姅 臧� 雼硠鞐愳劀 斓滌儊鞚� 鞎§厴鞚� 靹犿儩頃橃 鞎婈碃, 鞚检箻頃橂姅 頇曤 攵勴彫搿� 靸橅攲毵來枅鞀惦媹雼�. 臧€鞛� 雴掛潃 Q-Table 臧掛溂搿�, 頃儊 斓滌儊鞚� 鞎§厴鞚� 靹犿儩頃橂┐ 雿� 頃╇Μ鞝侅澑臧€鞖�? `np.argmax` 頃垬搿� 雴掛潃 Q-Table 臧掛棎 頃措嫻霅橂姅 鞎§厴 靾瀽毳� 彀眷晞靹� 毵堧毽暊 靾� 鞛堨姷雼堧嫟. 鞚� 鞝勲灥鞚� 甑槃頃橁碃 氚鸽煱鞀るゼ 臧滌劆頄堧姅歆€ 氪呺媹雼�.
-## [臧曥潣 頉� 韤挫](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/48/)
+## [臧曥潣 頉� 韤挫](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/48/)
## 瓿检牅: [Train a Mountain Car](../assignment.md)
diff --git a/8-Reinforcement/2-Gym/translations/README.zh-cn.md b/8-Reinforcement/2-Gym/translations/README.zh-cn.md
index 783c809f..9ff984d6 100644
--- a/8-Reinforcement/2-Gym/translations/README.zh-cn.md
+++ b/8-Reinforcement/2-Gym/translations/README.zh-cn.md
@@ -3,7 +3,7 @@
鎴戜滑鍦ㄤ笂涓€璇句腑涓€鐩村湪瑙ｅ喅鐨勯棶棰樺彲鑳界湅璧锋潵鍍忎竴涓帺鍏烽棶棰橈紝骞朵笉鐪熸閫傜敤浜庣幇瀹炵敓娲诲満鏅€備簨瀹炲苟闈炲姝わ紝鍥犱负璁稿鐜板疄涓栫晫鐨勯棶棰樹篃鏈夎繖绉嶆儏鍐碘€斺€斿寘鎷笅鍥介檯璞℃鎴栧洿妫嬨€傚畠浠緢鐩镐技锛屽洜涓烘垜浠篃鏈変竴涓叿鏈夌粰瀹氳鍒欏拰**绂绘暎鐘舵€�**鐨勬澘銆�
https://white-water-09ec41f0f.azurestaticapps.net/
-## [璇惧墠娴嬮獙](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/47/)
+## [璇惧墠娴嬮獙](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/47/)
## 浠嬬粛
@@ -330,7 +330,7 @@ env.close()
> **浠诲姟 4**锛氳繖閲屾垜浠笉鏄湪姣忎竴姝ラ€夋嫨鏈€浣冲姩浣滐紝鑰屾槸鐢ㄧ浉搴旂殑姒傜巼鍒嗗竷杩涜閲囨牱銆傚缁堥€夋嫨鍏锋湁鏈€楂� Q-Table 鍊肩殑鏈€浣冲姩浣滄槸鍚︽洿鏈夋剰涔夛紵杩欏彲浠ラ€氳繃浣跨敤 `np.argmax` 鍑芥暟鎵惧嚭瀵瑰簲浜庤緝楂� Q-Table 鍊肩殑鍔ㄤ綔缂栧彿鏉ュ畬鎴愩€傚疄鏂借繖涓瓥鐣ワ紝鐪嬬湅瀹冩槸鍚﹁兘鏀瑰杽骞宠 銆�
-## [璇惧悗娴嬮獙](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/48/)
+## [璇惧悗娴嬮獙](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/48/)
## 浣滀笟锛歔璁粌灞卞湴杞(assignment.zh-cn.md)
diff --git a/9-Real-World/1-Applications/README.md b/9-Real-World/1-Applications/README.md
index 7edc6aed..becfe1fe 100644
--- a/9-Real-World/1-Applications/README.md
+++ b/9-Real-World/1-Applications/README.md
@@ -8,7 +8,7 @@ In this curriculum, you have learned many ways to prepare data for training and
While a lot of interest in industry has been garnered by AI, which usually leverages deep learning, there are still valuable applications for classical machine learning models. You might even use some of these applications today! In this lesson, you'll explore how eight different industries and subject-matter domains use these types of models to make their applications more performant, reliable, intelligent, and valuable to users.
-## [Pre-lecture quiz](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/49/)
+## [Pre-lecture quiz](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/49/)
## 馃挵 Finance
@@ -152,7 +152,7 @@ https://ai.inqline.com/machine-learning-for-marketing-customer-segmentation/
Identify another sector that benefits from some of the techniques you learned in this curriculum, and discover how it uses ML.
-## [Post-lecture quiz](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/50/)
+## [Post-lecture quiz](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/50/)
## Review & Self Study
diff --git a/9-Real-World/1-Applications/translations/README.it.md b/9-Real-World/1-Applications/translations/README.it.md
index 180460dc..7b852038 100644
--- a/9-Real-World/1-Applications/translations/README.it.md
+++ b/9-Real-World/1-Applications/translations/README.it.md
@@ -7,7 +7,7 @@ In questo programma di studi si sono appresi molti modi per preparare i dati per
Sebbene l'intelligenza artificiale abbia suscitato molto interesse nell'industria, che di solito sfrutta il deep learning, esistono ancora preziose applicazioni per i modelli classici di machine learning. Si potrebbero anche usare alcune di queste applicazioni oggi! In questa lezione, si esplorer脿 come otto diversi settori e campi relativi all'argomento utilizzano questi tipi di modelli per rendere le loro applicazioni pi霉 performanti, affidabili, intelligenti e preziose per gli utenti.
-## [Quiz pre-lezione](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/49/?loc=it)
+## [Quiz pre-lezione](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/49/?loc=it)
## Finanza
@@ -151,7 +151,7 @@ https://ai.inqline.com/machine-learning-for-marketing-customer-segmentation/
Identificare un altro settore che beneficia di alcune delle tecniche apprese in questo programma di studi e scoprire come utilizza il machine learning.
-## [Quiz post-lezione](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/50/?loc=it)
+## [Quiz post-lezione](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/50/?loc=it)
## Revisione e Auto Apprendimento
diff --git a/9-Real-World/1-Applications/translations/README.ko.md b/9-Real-World/1-Applications/translations/README.ko.md
index 6c1a36a4..fccce7a0 100644
--- a/9-Real-World/1-Applications/translations/README.ko.md
+++ b/9-Real-World/1-Applications/translations/README.ko.md
@@ -8,7 +8,7 @@
氤错喌 霐ル煬雼濎潉 頇滌毄頃橂姅, AI搿� 靷办梾鞐� 毵庫潃 甏€鞁澊 氇澊歆€毵�, 鞐爠頌� classical 毹胳嫚霟嫕 氇嵏鞚� 臧€旃橃瀳電� 鞎犿攲毽紑鞚挫厴霃� 臁挫灛頃╇媹雼�. 鞓る姌 鞚� 鞎犿攲毽紑鞚挫厴 鞚茧秬毳� 靷毄頃� 靾橂弰 鞛堨姷雼堧嫟! 鞚� 臧曥潣鞐愳劀, 8臧� 雼れ枒頃� 靷办梾瓿� subject-matter 霃勲⿺鞚胳棎靹� 鞚� 氇嵏 韮€鞛呾溂搿� 鞎犿攲毽紑鞚挫厴鞚� 靹彪姤, 鞁犽, 歆€電リ臣, 靷毄鞛� 臧€旃橂ゼ 鞏措柣瓴� 雿� 雴掛澕歆€ 韮愳儔頃� 鞓堨爼鞛呺媹雼�.
-## [臧曥潣 鞝� 韤挫](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/49/)
+## [臧曥潣 鞝� 韤挫](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/49/)
## 馃挵 旮堨湹
@@ -152,7 +152,7 @@ https://ai.inqline.com/machine-learning-for-marketing-customer-segmentation/
鞚� 旎るΜ韥橂熂鞐愳劀 氚办洜雿� 鞚茧秬 旮办垹搿� 鞚挫澋鞚� 雮� 雼るジ 靸夗劙毳� 鞁濍硠頃橁碃, ML鞚� 鞏措柣瓴� 靷毄頃橂姅歆€ 韮愳儔頃╇媹雼�.
-## [臧曥潣 頉� 頃欖姷](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/50/)
+## [臧曥潣 頉� 頃欖姷](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/50/)
## 瓴€韱� & 鞛愱赴欤茧弰 頃欖姷
diff --git a/README.md b/README.md
index f80a71d7..0fce87fb 100644
--- a/README.md
+++ b/README.md
@@ -77,7 +77,7 @@ By ensuring that the content aligns with projects, the process is made more enga
> **A note about languages**: These lessons are primarily written in Python, but many are also available in R. To complete an R lesson, go to the `/solution` folder and look for R lessons. They include an .rmd extension that represents an **R Markdown** file which can be simply defined as an embedding of `code chunks` (of R or other languages) and a `YAML header` (that guides how to format outputs such as PDF) in a `Markdown document`. As such, it serves as an exemplary authoring framework for data science since it allows you to combine your code, its output, and your thoughts by allowing you to write them down in Markdown. Moreover, R Markdown documents can be rendered to output formats such as PDF, HTML, or Word.
-> **A note about quizzes**: All quizzes are contained [in this app](https://gentle-hill-034defd0f.1.azurestaticapps.net/), for 52 total quizzes of three questions each. They are linked from within the lessons but the quiz app can be run locally; follow the instruction in the `quiz-app` folder.
+> **A note about quizzes**: All quizzes are contained [in this app](https://gray-sand-07a10f403.1.azurestaticapps.net/), for 52 total quizzes of three questions each. They are linked from within the lessons but the quiz app can be run locally; follow the instruction in the `quiz-app` folder.
| Lesson Number | Topic | Lesson Grouping | Learning Objectives | Linked Lesson | Author |
| :-----------: | :------------------------------------------------------------: | :-------------------------------------------------: | ------------------------------------------------------------------------------------------------------------------------------- | :--------------------------------------------------------------------------------------------------------------------------------------: | :--------------------------------------------------: |
diff --git a/TRANSLATIONS.md b/TRANSLATIONS.md
index 5e58778e..0385169c 100644
--- a/TRANSLATIONS.md
+++ b/TRANSLATIONS.md
@@ -27,7 +27,7 @@ Similar to Readme's, please translate the assignments as well.
3. Edit the quiz-app's [translations index.js file](https://github.com/microsoft/ML-For-Beginners/blob/main/quiz-app/src/assets/translations/index.js) to add your language.
-4. Finally, edit ALL the quiz links in your translated README.md files to point directly to your translated quiz: https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/1 becomes https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/1?loc=id
+4. Finally, edit ALL the quiz links in your translated README.md files to point directly to your translated quiz: https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/1 becomes https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/1?loc=id
**THANK YOU**
diff --git a/translations/README.es.md b/translations/README.es.md
index 49e0f274..03419a77 100644
--- a/translations/README.es.md
+++ b/translations/README.es.md
@@ -84,7 +84,7 @@ Al asegurar que el contenido se alinea con los proyectos, el proceso se hace m谩
> **Una nota acerca de los lenguajes**: Estas lecciones est谩n escritas principalmente en Python, pero muchas tambi茅n est谩n disponibles en R. Para completar una lecci贸n en R, ve al directorio `/solution` y busca las lecciones. Ellas incluyen una extensi贸n .rmd que representa un archivo **Markdown R** el cual puede ser definido simplemente como `porciones de c贸digo` embebido (de R u otros lenguajes) y un `encabezado YAML` (que gu铆a c贸mo dar formato a las salidas, por ejemplo PDF) en un `documento Markdown`. Como tal, este sirve como un framework ejemplar de autor铆a para la ciencia de datos ya que permite combinar tu c贸digo, su salida, y tus pensamientos al permitirte escribirlos en Markdown. Es m谩s, los documentos Markdown R pueden ser representados como formatos de salida tal como PDF, HTML, o Word.
-> **Una nota acerca de los ex谩menes**: Todos los ex谩menes est谩n contenidos [en esta app](https://gentle-hill-034defd0f.1.azurestaticapps.net/), para un total de 52 ex谩menes de 3 preguntas cada uno, Ellos est谩n vinculados dentro de las lecciones pero la aplicaci贸n de ex谩menes puede ser ejecutada localmente; sigue las instrucciones en el directorio `quiz-app`.
+> **Una nota acerca de los ex谩menes**: Todos los ex谩menes est谩n contenidos [en esta app](https://gray-sand-07a10f403.1.azurestaticapps.net/), para un total de 52 ex谩menes de 3 preguntas cada uno, Ellos est谩n vinculados dentro de las lecciones pero la aplicaci贸n de ex谩menes puede ser ejecutada localmente; sigue las instrucciones en el directorio `quiz-app`.
| N煤mero de lecci贸n | Tema | Agrupaci贸n de lecciones | Objetivos de aprendizaje | Lecci贸n vinculada | Autor |
| :-----------: | :------------------------------------------------------------: | :-------------------------------------------------: | ------------------------------------------------------------------------------------------------------------------------------- | :--------------------------------------------------------------------------------------------------------------------------------------: | :--------------------------------------------------: |
diff --git a/translations/README.hi.md b/translations/README.hi.md
index e44731fd..de8835c6 100644
--- a/translations/README.hi.md
+++ b/translations/README.hi.md
@@ -77,7 +77,7 @@
> **啶ぞ啶粪ぞ啶撪 啶曕 啶ぞ啶班 啶啶� 啶忇 啶ㄠ啶�**: 啶 啶ぞ啶� 啶啶栢啶� 啶班啶� 啶膏 啶ぞ啶ぅ啶� 啶啶� 啶侧た啶栢 啶椸 啶灌啶�, 啶侧啶曕た啶� 啶曕 啶嗋ぐ 啶啶� 啶 啶夃お啶侧が啷嵿ぇ 啶灌啶傕イ 啶忇 啶嗋ぐ 啶ぞ啶� 啶曕 啶啶班ぞ 啶曕ぐ啶ㄠ 啶曕 啶侧た啶�, `/ 啶膏ぎ啶距ぇ啶距え` 啶ぜ啷嬥げ啷嵿ぁ啶� 啶啶� 啶溹ぞ啶忇 啶斷ぐ 啶嗋ぐ 啶ぞ啶� 啶︵啶栢啶傕イ 啶夃え啶啶� 啶忇 .rmd 啶忇啷嵿じ啶熰啶傕ざ啶� 啶多ぞ啶た啶� 啶灌 啶溹 啶忇 **R 啶ぞ啶班啶曕ぁ啶距啶�** 啶ぜ啶距啶� 啶曕ぞ 啶啶班い啶苦え啶苦ぇ啶苦い啷嵿さ 啶曕ぐ啶むぞ 啶灌 啶溹た啶膏 啶曕啶掂げ `啶曕啶� 啶氞啶曕啶竊 (啶嗋ぐ 啶ぞ 啶呧え啷嵿く 啶ぞ啶粪ぞ啶撪 啶曕) 啶曕 啶忇ぎ啷嵿が啷囙ぁ啶苦啶� 啶曕 啶班啶� 啶啶� 啶ぐ啶苦き啶距し啶苦い 啶曕た啶ぞ 啶溹ぞ 啶膏啶むぞ 啶灌 啶斷ぐ 啶忇 `啶掂ぞ啶堗啶忇ぎ啶忇げ 啶灌啶∴ぐ` (啶溹 啶囙じ 啶むぐ啶� 啶曕 啶嗋啶熰お啷佮 啶曕 啶啶班ぞ啶班啶た啶� 啶曕ぐ啶ㄠ 啶曕ぞ 啶ぞ啶班啶椸う啶班啶多え 啶曕ぐ啶むぞ 啶灌) 啶啶∴啶忇か 啶曕 啶班啶� 啶啶�) 啶忇 `啶ぞ啶班啶曕ぁ啶距啶� 啶︵じ啷嵿い啶距さ啷囙啶糮 啶啶傕イ 啶溹啶膏, 啶す 啶∴啶熰ぞ 啶掂た啶溹啶炧ぞ啶� 啶曕 啶侧た啶� 啶忇 啶呧え啷佮啶班ぃ啷€啶� 啶膏啶侧啶栢え 啶⑧ぞ啶傕啷� 啶曕 啶班啶� 啶啶� 啶曕ぞ啶班啶� 啶曕ぐ啶むぞ 啶灌 啶曕啶啶傕啶� 啶す 啶嗋お啶曕 啶呧お啶ㄠ 啶曕啶�, 啶囙じ啶曕 啶嗋啶熰お啷佮 啶斷ぐ 啶嗋お啶曕 啶掂た啶氞ぞ啶班啶� 啶曕 啶ぞ啶班啶曕ぁ啶距啶� 啶啶� 啶侧た啶栢え啷� 啶曕 啶呧え啷佮ぎ啶むた 啶︵啶曕ぐ 啶嗋お啶曕 啶膏啶啶溹た啶� 啶曕ぐ啶ㄠ 啶曕 啶呧え啷佮ぎ啶むた 啶︵啶むぞ 啶灌啷� 啶囙じ啶曕 啶呧げ啶距さ啶�, 啶嗋ぐ 啶ぞ啶班啶曕ぁ啶距啶� 啶︵じ啷嵿い啶距さ啷囙啷嬥 啶曕 啶啶∴啶忇か, 啶忇啶熰啶忇ぎ啶忇げ 啶ぞ 啶掂ぐ啷嵿ぁ 啶溹啶膏 啶嗋啶熰お啷佮 啶膏啶掂ぐ啷傕お啷嬥 啶啶� 啶啶班じ啷嵿い啷佮い 啶曕た啶ぞ 啶溹ぞ 啶膏啶むぞ 啶灌啷�
-> **啶曕啶掂た啶溹ぜ 啶曕 啶ぞ啶班 啶啶� 啶忇 啶ㄠ啶�**: 啶膏き啷€ 啶曕啶掂た啶溹ぜ 啶多ぞ啶た啶� 啶灌啶� [啶囙じ 啶愢お 啶啶俔(https://gentle-hill-034defd0f.1.azurestaticapps.net/), 啶啶班い啷嵿く啷囙 啶む啶� 啶啶班ざ啷嵿え啷嬥 啶曕 啶曕啶� 52 啶曕啶掂た啶溹ぜ 啶曕 啶侧た啶忇イ 啶掂 啶ぞ啶犩啶� 啶曕 啶啶むぐ 啶膏 啶溹啶∴ぜ啷� 啶灌啶� 啶灌啶� 啶侧啶曕た啶� 啶啶班ざ啷嵿え啷嬥い啷嵿い啶班 啶愢お 啶曕 啶膏啶ムぞ啶ㄠ啶� 啶班啶� 啶膏 啶氞げ啶距く啶� 啶溹ぞ 啶膏啶むぞ 啶灌; `啶曕啶掂た啶溹ぜ-啶愢お` 啶ぜ啷嬥げ啷嵿ぁ啶� 啶啶� 啶︵た啶� 啶椸 啶ㄠた啶班啶︵啶多啶� 啶曕ぞ 啶ぞ啶侧え 啶曕ぐ啷囙啷�
+> **啶曕啶掂た啶溹ぜ 啶曕 啶ぞ啶班 啶啶� 啶忇 啶ㄠ啶�**: 啶膏き啷€ 啶曕啶掂た啶溹ぜ 啶多ぞ啶た啶� 啶灌啶� [啶囙じ 啶愢お 啶啶俔(https://gray-sand-07a10f403.1.azurestaticapps.net/), 啶啶班い啷嵿く啷囙 啶む啶� 啶啶班ざ啷嵿え啷嬥 啶曕 啶曕啶� 52 啶曕啶掂た啶溹ぜ 啶曕 啶侧た啶忇イ 啶掂 啶ぞ啶犩啶� 啶曕 啶啶むぐ 啶膏 啶溹啶∴ぜ啷� 啶灌啶� 啶灌啶� 啶侧啶曕た啶� 啶啶班ざ啷嵿え啷嬥い啷嵿い啶班 啶愢お 啶曕 啶膏啶ムぞ啶ㄠ啶� 啶班啶� 啶膏 啶氞げ啶距く啶� 啶溹ぞ 啶膏啶むぞ 啶灌; `啶曕啶掂た啶溹ぜ-啶愢お` 啶ぜ啷嬥げ啷嵿ぁ啶� 啶啶� 啶︵た啶� 啶椸 啶ㄠた啶班啶︵啶多啶� 啶曕ぞ 啶ぞ啶侧え 啶曕ぐ啷囙啷�
| 啶ぞ啶� 啶膏啶栢啶ぞ | 啶掂た啶粪く | 啶ぞ啶� 啶膏ぎ啷傕す啶� | 啶膏啶栢え啷� 啶曕 啶啶膏う | 啶溹啶∴ぜ啶� 啶灌啶� 啶ぞ啶� | 啶侧啶栢 |
| :-----------: | :------------------------------------------------------------: | :-------------------------------------------------: | ------------------------------------------------------------------------------------------------------------------------------- | :--------------------------------------------------------------------------------------------------------------------------------------: | :--------------------------------------------------: |
diff --git a/translations/README.it.md b/translations/README.it.md
index cc52c583..1bf67871 100644
--- a/translations/README.it.md
+++ b/translations/README.it.md
@@ -73,7 +73,7 @@ Assicurandosi che il contenuto si allinei con i progetti, il processo 猫 reso pi
- un compito
- un quiz post-lezione
-> **Una nota sui quiz**: Tutti i quiz sono contenuti [in questa app](https://gentle-hill-034defd0f.1.azurestaticapps.net/), per un totale di 50 quiz con tre domande ciascuno. I link ai quiz sono presenti all'interno delle lezioni ma l'app pu貌 essere eseguita in locale seguendo le istruzioni contenute nella cartella `quiz-app`.
+> **Una nota sui quiz**: Tutti i quiz sono contenuti [in questa app](https://gray-sand-07a10f403.1.azurestaticapps.net/), per un totale di 50 quiz con tre domande ciascuno. I link ai quiz sono presenti all'interno delle lezioni ma l'app pu貌 essere eseguita in locale seguendo le istruzioni contenute nella cartella `quiz-app`.
| Numero Lezione | Argomento | Gruppo Lezioni | Obiettivi di Apprendimento | Lezioni Collegate | Autore |
| :-----------: | :--------------------------------------------------------: | :-------------------------------------------------: | ------------------------------------------------------------------------------------------------------------------------------- | :---------------------------------------------------: | :------------: |
diff --git a/translations/README.ja.md b/translations/README.ja.md
index 2da93492..35faffdb 100644
--- a/translations/README.ja.md
+++ b/translations/README.ja.md
@@ -71,7 +71,7 @@
- 瑾查
- 璎涚京寰屻伄灏忋儐銈广儓
-> **灏忋儐銈广儓銇枹銇欍倠娉ㄦ剰**: 銇欍伖銇︺伄灏忋儐銈广儓銇� [銇撱伄銈€儣銉猐(https://gentle-hill-034defd0f.1.azurestaticapps.net/) 銇惈銇俱倢銇︺亰銈娿€佸悇3鍟忋亱銈夈仾銈�50鍊嬨伄灏忋儐銈广儓銇屻亗銈娿伨銇欍€傘亾銈屻倝銇儸銉冦偣銉冲唴銇嬨倝銉兂銈仌銈屻仸銇勩伨銇欍亴銆併偄銉椼儶銈掋儹銉笺偒銉仹瀹熻銇欍倠銇撱仺銈傘仹銇嶃伨銇欍€俙quiz-app` 銉曘偐銉儉鍐呫伄鎸囩ず銇緭銇ｃ仸銇忋仩銇曘亜銆�
+> **灏忋儐銈广儓銇枹銇欍倠娉ㄦ剰**: 銇欍伖銇︺伄灏忋儐銈广儓銇� [銇撱伄銈€儣銉猐(https://gray-sand-07a10f403.1.azurestaticapps.net/) 銇惈銇俱倢銇︺亰銈娿€佸悇3鍟忋亱銈夈仾銈�50鍊嬨伄灏忋儐銈广儓銇屻亗銈娿伨銇欍€傘亾銈屻倝銇儸銉冦偣銉冲唴銇嬨倝銉兂銈仌銈屻仸銇勩伨銇欍亴銆併偄銉椼儶銈掋儹銉笺偒銉仹瀹熻銇欍倠銇撱仺銈傘仹銇嶃伨銇欍€俙quiz-app` 銉曘偐銉儉鍐呫伄鎸囩ず銇緭銇ｃ仸銇忋仩銇曘亜銆�
| 銉儍銈广兂鐣彿 | 銉堛償銉冦偗 | 銉儍銈广兂銈般儷銉笺儣 | 瀛︾繏銇洰鐨� | 闁㈤€ｃ仚銈嬨儸銉冦偣銉� | 钁楄€� |
| :----------: | :------------------------------------------: | :----------------------------------------------------: | ------------------------------------------------------------------------------------------ | :---------------------------------------------------------------------: | :------------: |
diff --git a/translations/README.ko.md b/translations/README.ko.md
index f6df8cac..5602333b 100644
--- a/translations/README.ko.md
+++ b/translations/README.ko.md
@@ -73,7 +73,7 @@ Microsoft鞚� Azure Cloud Advocates電� **Machine Learning**鞐� 雽€頃� 氇摖 12-
- 瓿检牅
- 臧曥潣 頉� 韤挫
-> **韤挫 彀戈碃靷暛**: 氇摖 韤挫電� [鞚� 鞎盷(https://gentle-hill-034defd0f.1.azurestaticapps.net/)鞐� 韽暔霅橃柎 鞛堨溂氅�, 臧侁皝 3氍胳牅鞌� 齑� 50臧滌潣 韤挫臧€ 鞛堨姷雼堧嫟. 韤挫 鞎膘潃 甑愳湣 瓿检爼瓿� 鞐瓣舶霅橃柎 鞛堨毵�, 鞗愴晿電� 瓴届毎 霐半 韤挫 鞎膘潉 鞁ろ枆頃� 靾橂弰 鞛堨姷雼堧嫟. 鞛愳劯頃� 靷暛鞚€ 韤挫 鞎� 韽措崝 雮挫潣 歆€旃潉 霐半ゴ鞁嫓鞓�.
+> **韤挫 彀戈碃靷暛**: 氇摖 韤挫電� [鞚� 鞎盷(https://gray-sand-07a10f403.1.azurestaticapps.net/)鞐� 韽暔霅橃柎 鞛堨溂氅�, 臧侁皝 3氍胳牅鞌� 齑� 50臧滌潣 韤挫臧€ 鞛堨姷雼堧嫟. 韤挫 鞎膘潃 甑愳湣 瓿检爼瓿� 鞐瓣舶霅橃柎 鞛堨毵�, 鞗愴晿電� 瓴届毎 霐半 韤挫 鞎膘潉 鞁ろ枆頃� 靾橂弰 鞛堨姷雼堧嫟. 鞛愳劯頃� 靷暛鞚€ 韤挫 鞎� 韽措崝 雮挫潣 歆€旃潉 霐半ゴ鞁嫓鞓�.
| 臧曥潣 氩堩樃 | 韱犿斀 | 臧曥潣 攴鸽９ | 頃欖姷 氇╉憸 | 鞐瓣舶 臧曥潣 | 鞝€鞛� |
diff --git a/translations/README.ms.md b/translations/README.ms.md
index 3b861ffd..982245d5 100644
--- a/translations/README.ms.md
+++ b/translations/README.ms.md
@@ -76,7 +76,7 @@ Dengan memastikan bahawa kandungan sesuai dengan projek, proses dibuat lebih men
> **Catatan mengenai bahasa**: Pelajaran ini terutama ditulis dalam Python, tetapi banyak juga tersedia dalam R. Untuk menyelesaikan pelajaran R, pergi ke folder `/solution` dan cari pelajaran R. Mereka termasuk pelanjutan .rmd yang mewakili fail **R Markdown** yang hanya dapat didefinisikan sebagai penyisipan `potongan kode '(dari R atau bahasa lain) dan` header YAML` (yang membimbing cara memformat output seperti PDF) dalam `Markdown document`. Oleh itu, ia berfungsi sebagai kerangka penulisan teladan bagi sains data kerana ia membolehkan anda menggabungkan kod, output dan pemikiran anda dengan membolehkan anda menuliskannya dalam Markdown. Lebih-lebih lagi, dokumen R Markdown dapat diberikan ke format output seperti PDF, HTML, atau Word.
-> **Catatan mengenai kuiz**: Semua kuiz terkandung [dalam aplikasi ini](https://gentle-hill-034defd0f.1.azurestaticapps.net/), untuk 50 keseluruhan kuiz masing-masing dari tiga soalan. Mereka dihubungkan dari dalam pelajaran tetapi aplikasi kuiz dapat dijalankan secara tempatan; ikuti arahan dalam folder `quiz-app`.
+> **Catatan mengenai kuiz**: Semua kuiz terkandung [dalam aplikasi ini](https://gray-sand-07a10f403.1.azurestaticapps.net/), untuk 50 keseluruhan kuiz masing-masing dari tiga soalan. Mereka dihubungkan dari dalam pelajaran tetapi aplikasi kuiz dapat dijalankan secara tempatan; ikuti arahan dalam folder `quiz-app`.
| Nombor Pelajaran | Topik | Pengumpulan Pelajaran | Objektif Pembelajaran | Pautan Pembelajaran | Pengarang |
diff --git a/translations/README.pt-br.md b/translations/README.pt-br.md
index b4385b27..0c2b0831 100644
--- a/translations/README.pt-br.md
+++ b/translations/README.pt-br.md
@@ -73,7 +73,7 @@ Ao garantir que o conte煤do esteja alinhado com os projetos, o processo torna-se
- tarefa
- question谩rio p贸s-aula
-> **Uma nota sobre question谩rios**: Todos os question谩rios est茫o contidos [neste aplicativo](https://gentle-hill-034defd0f.1.azurestaticapps.net/), para um total de 50 testes de tr锚s perguntas cada. Eles est茫o vinculados nas li莽玫es, mas o aplicativo de teste pode ser executado localmente; siga as instru莽玫es na pasta `quiz-app`.
+> **Uma nota sobre question谩rios**: Todos os question谩rios est茫o contidos [neste aplicativo](https://gray-sand-07a10f403.1.azurestaticapps.net/), para um total de 50 testes de tr锚s perguntas cada. Eles est茫o vinculados nas li莽玫es, mas o aplicativo de teste pode ser executado localmente; siga as instru莽玫es na pasta `quiz-app`.
| N煤mero da aula | T贸pico | Agrupamento de Aulas | Objetivos de aprendizagem | Aula vinculada | Autor |
| :------------: | :-------------------------------------------------------------------: | :---------------------------------------------------: | ------------------------------------------------------------------------------------------------------------------------------------ | :-------------------------------------------------: | :----------: |
diff --git a/translations/README.pt.md b/translations/README.pt.md
index ee0386ee..6a6ceb78 100644
--- a/translations/README.pt.md
+++ b/translations/README.pt.md
@@ -71,7 +71,7 @@ Ao garantir que o conte煤do esteja alinhado com os projetos, o processo torna-se
- tarefa
- teste p贸s-aula
-> **Uma nota sobre testes**: Podes encontrar todos os testes [nesta app](https://gentle-hill-034defd0f.1.azurestaticapps.net/), para um total de 50 testes de 3 perguntas cada. Eles est茫o vinculados 脿s aulas, mas a aplica莽茫o do teste pode ser executada localmente; segue as intru莽玫es na pasta `quiz-app`.
+> **Uma nota sobre testes**: Podes encontrar todos os testes [nesta app](https://gray-sand-07a10f403.1.azurestaticapps.net/), para um total de 50 testes de 3 perguntas cada. Eles est茫o vinculados 脿s aulas, mas a aplica莽茫o do teste pode ser executada localmente; segue as intru莽玫es na pasta `quiz-app`.
| N煤mero de aula | T贸pico | Agrupamento de Aulas | Objetivos de aprendizagem | Aula vinculada | Autor |
| :------------: | :-------------------------------------------------------------------: | :---------------------------------------------------: | ------------------------------------------------------------------------------------------------------------------------------------ | :-------------------------------------------------: | :----------: |
diff --git a/translations/README.tr.md b/translations/README.tr.md
index 68a49be4..c04ce5d9 100644
--- a/translations/README.tr.md
+++ b/translations/README.tr.md
@@ -68,7 +68,7 @@ Bu e臒itim program谋n谋 olu艧tururken iki pedagojik ilke se莽tik: uygulamal谋 **
- 枚dev
- ders sonras谋 k谋sa s谋nav谋
-> **K谋sa s谋navlar hakk谋nda bir not**: Her biri 眉莽 sorudan olu艧an ve toplamda 50 tane olan t眉m k谋sa s谋navlar [bu uygulamada](https://gentle-hill-034defd0f.1.azurestaticapps.net/) bulunmaktad谋r. Derslerin i莽inden de ba臒lant谋 yoluyla ula艧谋labilirler ancak k谋sa s谋nav uygulamas谋 yerelde 莽al谋艧t谋r谋labilir; `quiz-app` klas枚r眉ndeki y枚nergeleri takip edin.
+> **K谋sa s谋navlar hakk谋nda bir not**: Her biri 眉莽 sorudan olu艧an ve toplamda 50 tane olan t眉m k谋sa s谋navlar [bu uygulamada](https://gray-sand-07a10f403.1.azurestaticapps.net/) bulunmaktad谋r. Derslerin i莽inden de ba臒lant谋 yoluyla ula艧谋labilirler ancak k谋sa s谋nav uygulamas谋 yerelde 莽al谋艧t谋r谋labilir; `quiz-app` klas枚r眉ndeki y枚nergeleri takip edin.
| Ders Numaras谋 | Konu | Ders Grupland谋rmas谋 | 脰臒renme Hedefleri | Ders | Yazar |
diff --git a/translations/README.zh-cn.md b/translations/README.zh-cn.md
index 192311d1..b93625aa 100644
--- a/translations/README.zh-cn.md
+++ b/translations/README.zh-cn.md
@@ -69,7 +69,7 @@
- 浣滀笟
- 璇惧悗娴嬮獙
-> **鍏充簬娴嬮獙**锛氭墍鏈夌殑娴嬮獙閮藉湪[杩欎釜搴旂敤閲宂(https://gentle-hill-034defd0f.1.azurestaticapps.net/)锛屾€诲叡 50 涓祴楠岋紝姣忎釜娴嬮獙涓変釜闂銆傚畠浠殑閾炬帴鍦ㄦ瘡鑺傝涓紝鑰屼笖杩欎釜娴嬮獙搴旂敤鍙互鍦ㄦ湰鍦拌繍琛屻€傝鍙傝€� `quiz-app` 鏂囦欢澶逛腑鐨勬寚鍗椼€�
+> **鍏充簬娴嬮獙**锛氭墍鏈夌殑娴嬮獙閮藉湪[杩欎釜搴旂敤閲宂(https://gray-sand-07a10f403.1.azurestaticapps.net/)锛屾€诲叡 50 涓祴楠岋紝姣忎釜娴嬮獙涓変釜闂銆傚畠浠殑閾炬帴鍦ㄦ瘡鑺傝涓紝鑰屼笖杩欎釜娴嬮獙搴旂敤鍙互鍦ㄦ湰鍦拌繍琛屻€傝鍙傝€� `quiz-app` 鏂囦欢澶逛腑鐨勬寚鍗椼€�
| 璇剧▼缂栧彿 | 涓讳綋 | 璇剧▼缁� | 瀛︿範鐩爣 | 璇剧▼閾炬帴 | 浣滆€� |
diff --git a/translations/Readme.ta.md b/translations/Readme.ta.md
index 56c00fa5..52acd885 100644
--- a/translations/Readme.ta.md
+++ b/translations/Readme.ta.md
@@ -80,7 +80,7 @@
> `YAML 喈む喁堗喁嵿喁乣 (PDF 喈瘚喈┼瘝喈� 喈掂瘑喈赤喈瘈喈熰瘉喈曕喁� 喈庎喁嵿喈熰 喈掂疅喈苦喈瘓喈瘝喈喁� 喈庎喁嵿喁� 喈掂喈苦畷喈距疅喁嵿疅喁佮畷喈苦喈む瘉)
> 喈掄喁� `喈喈班瘝喈曕瘝 喈熰喁佮喁� 喈嗋喈｀喁峘. 喈庎喈掂瘒, 喈囙喁� 喈む喈掂瘉 喈呧喈苦喈苦喈侧瘉喈曕瘝喈曕喈� 喈瘉喈┼瘝喈喈む喈班喈喈� 喈嗋畾喈苦喈苦喈班瘝 喈曕疅喁嵿疅喈瘓喈瘝喈喈� 喈氞瘑喈喁嵿喈熰瘉喈曕喈编喁�, 喈忇喁嗋喈苦喁� 喈囙喁� 喈夃畽喁嵿畷喈赤瘝 喈曕瘉喈编喈瘈喈熰瘉, 喈呧喈┼瘝 喈掂瘑喈赤喈瘈喈熰瘉 喈喁嵿喁佮喁� 喈夃畽喁嵿畷喈赤瘝 喈庎喁嵿喈權瘝喈曕喁� 喈喈班瘝喈曕瘝 喈熰喁佮喈苦喁� 喈庎喁佮 喈呧喁佮喈む喈瘝喈喈┼瘝 喈瘋喈侧喁� 喈掄喁佮畽喁嵿畷喈苦喁堗畷喁嵿畷 喈呧喁佮喈む喈曕瘝喈曕喈编喁�. 喈瘒喈侧瘉喈瘝, R 喈喈班瘝喈曕瘝 喈熰喁佮喁� 喈嗋喈｀畽喁嵿畷喈赤瘝 PDF, HTML 喈呧喁嵿喈む瘉 Word 喈瘚喈┼瘝喈� 喈掂瘑喈赤喈瘈喈熰瘝喈熰瘉 喈掂疅喈苦喈權瘝喈曕喁佮畷喁嵿畷喁� 喈掂喈權瘝喈曕喁嵿喈熰喈距喁�.
-> 喈掂喈┼喈熰 喈掂喈┼ 喈喁嵿喈苦 喈曕瘉喈编喈瘝喈瘉: 喈呧喁堗喁嵿喁� 喈掂喈┼喈熰 喈掂喈┼喈曕瘝喈曕喁佮喁� 喈呧疅喈權瘝喈曕喈瘉喈赤瘝喈赤 [喈囙喁嵿 喈喈┼瘝喈喈熰瘝喈熰喈侧瘝](https://gentle-hill-034defd0f.1.azurestaticapps.net/), 喈む喈� 喈瘋喈┼瘝喈编瘉 喈曕瘒喈赤瘝喈掂喈曕喁� 喈曕瘖喈｀瘝喈� 52 喈瘖喈む瘝喈� 喈掂喈┼喈熰 喈掂喈┼喈曕瘝喈曕喁佮畷喁嵿畷喁�. 喈呧喁� 喈喈熰畽喁嵿畷喈赤瘉喈曕瘝喈曕瘉喈赤瘝 喈囙喁佮喁嵿喁� 喈囙喁堗畷喁嵿畷喈瘝喈疅喁嵿疅喁佮喁嵿喈� 喈嗋喈距喁� 喈掂喈┼喈熰 喈掂喈┼ 喈喈┼瘝喈喈熰瘝喈熰瘓 喈夃喁嵿喈距疅喁嵿疅喈苦喁� 喈囙喈曕瘝喈� 喈瘉喈熰喈瘉喈瘝; 喈囙喁� 喈夃喁嵿 喈掂喈苦喁佮喁堗畷喈赤瘓喈瘝 喈喈┼瘝喈喁嵿喈掂瘉喈瘝 `喈掂喈┼喈熰 喈掂喈┼-喈喈┼瘝喈喈熰瘉`.
+> 喈掂喈┼喈熰 喈掂喈┼ 喈喁嵿喈苦 喈曕瘉喈编喈瘝喈瘉: 喈呧喁堗喁嵿喁� 喈掂喈┼喈熰 喈掂喈┼喈曕瘝喈曕喁佮喁� 喈呧疅喈權瘝喈曕喈瘉喈赤瘝喈赤 [喈囙喁嵿 喈喈┼瘝喈喈熰瘝喈熰喈侧瘝](https://gray-sand-07a10f403.1.azurestaticapps.net/), 喈む喈� 喈瘋喈┼瘝喈编瘉 喈曕瘒喈赤瘝喈掂喈曕喁� 喈曕瘖喈｀瘝喈� 52 喈瘖喈む瘝喈� 喈掂喈┼喈熰 喈掂喈┼喈曕瘝喈曕喁佮畷喁嵿畷喁�. 喈呧喁� 喈喈熰畽喁嵿畷喈赤瘉喈曕瘝喈曕瘉喈赤瘝 喈囙喁佮喁嵿喁� 喈囙喁堗畷喁嵿畷喈瘝喈疅喁嵿疅喁佮喁嵿喈� 喈嗋喈距喁� 喈掂喈┼喈熰 喈掂喈┼ 喈喈┼瘝喈喈熰瘝喈熰瘓 喈夃喁嵿喈距疅喁嵿疅喈苦喁� 喈囙喈曕瘝喈� 喈瘉喈熰喈瘉喈瘝; 喈囙喁� 喈夃喁嵿 喈掂喈苦喁佮喁堗畷喈赤瘓喈瘝 喈喈┼瘝喈喁嵿喈掂瘉喈瘝 `喈掂喈┼喈熰 喈掂喈┼-喈喈┼瘝喈喈熰瘉`.
| 喈喈熰喁� 喈庎喁� | 喈む喁堗喁嵿喁� | 喈喈熰喁� 喈む瘖喈曕瘉喈む瘝喈む喁� | 喈曕喁嵿喈侧瘝 喈ㄠ瘒喈距畷喁嵿畷喈權瘝喈曕喁� | 喈囙喁堗畷喁嵿畷喈瘝喈疅喁嵿疅 喈喈熰喁� | 喈ㄠ瘋喈侧喈氞喈班喈喁� |
| :--------: | :-----------------------------------------: | :----------------------------------------------------: | --------------------------------------------------------------------------------------------------------------------------------------------- | :-----------------------------------------------------: | :-----------------: |
--
GitLab