diff --git a/samples/Directory.Build.props b/samples/Directory.Build.props
index c93884706..e9ef49c7f 100644
--- a/samples/Directory.Build.props
+++ b/samples/Directory.Build.props
@@ -1,8 +1,8 @@
- 1.2.0
- 0.14.0
+ 1.1.0
+ 0.13.0
diff --git a/samples/csharp/end-to-end-apps/AnomalyDetection-Sales/README.md b/samples/csharp/end-to-end-apps/AnomalyDetection-Sales/README.md
index 9d457a515..4bc8bf696 100644
--- a/samples/csharp/end-to-end-apps/AnomalyDetection-Sales/README.md
+++ b/samples/csharp/end-to-end-apps/AnomalyDetection-Sales/README.md
@@ -2,7 +2,7 @@
| ML.NET version | API type | Status | App Type | Data type | Scenario | ML Task | Algorithms |
|----------------|-------------------|-------------------------------|-------------|-----------|---------------------|---------------------------|-----------------------------|
-| v1.2.0 | Dynamic API | Up-to-date | WinForms app | .csv files | Spike and Change Point Detection of Product Sales | Anomaly Detection | IID Spike Detection and IID Change point Detection |
+| v1.1.0 | Dynamic API | Up-to-date | WinForms app | .csv files | Spike and Change Point Detection of Product Sales | Anomaly Detection | IID Spike Detection and IID Change point Detection |
![Alt Text](./SpikeDetectionE2EApp/SpikeDetection.WinForms/images/productsales.gif)
diff --git a/samples/csharp/end-to-end-apps/DeepLearning_ImageClassification_TensorFlow/README.md b/samples/csharp/end-to-end-apps/DeepLearning_ImageClassification_TensorFlow/README.md
index 8f0810ae9..0358eb947 100644
--- a/samples/csharp/end-to-end-apps/DeepLearning_ImageClassification_TensorFlow/README.md
+++ b/samples/csharp/end-to-end-apps/DeepLearning_ImageClassification_TensorFlow/README.md
@@ -2,7 +2,7 @@
| ML.NET version | API type | Status | App Type | Data type | Scenario | ML Task | Algorithms |
|----------------|-------------------|-------------------------------|-------------|-----------|---------------------|---------------------------|-----------------------------|
-| v1.2.0 | Dynamic API | up-to-date | Console app | Images and text labels | Images classification | TensorFlow model | DeepLearning model |
+| v1.1.0 | Dynamic API | up-to-date | Console app | Images and text labels | Images classification | TensorFlow model | DeepLearning model |
## Problem
diff --git a/samples/csharp/end-to-end-apps/DeepLearning_ImageClassification_TensorFlow/TensorFlowImageClassification/TensorFlowImageClassification.csproj b/samples/csharp/end-to-end-apps/DeepLearning_ImageClassification_TensorFlow/TensorFlowImageClassification/TensorFlowImageClassification.csproj
index 1120d25b4..c15cdc5a9 100644
--- a/samples/csharp/end-to-end-apps/DeepLearning_ImageClassification_TensorFlow/TensorFlowImageClassification/TensorFlowImageClassification.csproj
+++ b/samples/csharp/end-to-end-apps/DeepLearning_ImageClassification_TensorFlow/TensorFlowImageClassification/TensorFlowImageClassification.csproj
@@ -9,10 +9,10 @@
-
-
-
-
+
+
+
+
diff --git a/samples/csharp/end-to-end-apps/DeepLearning_ObjectDetection_Onnx/README.md b/samples/csharp/end-to-end-apps/DeepLearning_ObjectDetection_Onnx/README.md
index 191a7010c..642618c77 100644
--- a/samples/csharp/end-to-end-apps/DeepLearning_ObjectDetection_Onnx/README.md
+++ b/samples/csharp/end-to-end-apps/DeepLearning_ObjectDetection_Onnx/README.md
@@ -2,7 +2,7 @@
| ML.NET version | API type | Status | App Type | Data type | Scenario | ML Task | Algorithms |
|----------------|-------------------|-------------------------------|-------------|-----------|---------------------|---------------------------|-----------------------------|
-| v1.2.0 | Dynamic API | Up-to-date | End-End app | image files | Object Detection | Deep Learning | Tiny Yolo2 ONNX model |
+| v1.1.0 | Dynamic API | Up-to-date | End-End app | image files | Object Detection | Deep Learning | Tiny Yolo2 ONNX model |
## Problem
Object detection is one of the classical problems in computer vision: Recognize what objects are inside a given image and also where they are in the image. For these cases, you can either use pre-trained models or train your own model to classify images specific to your custom domain.
diff --git a/samples/csharp/end-to-end-apps/MulticlassClassification-GitHubLabeler/README.md b/samples/csharp/end-to-end-apps/MulticlassClassification-GitHubLabeler/README.md
index 1fb8f74ce..caeca35f1 100644
--- a/samples/csharp/end-to-end-apps/MulticlassClassification-GitHubLabeler/README.md
+++ b/samples/csharp/end-to-end-apps/MulticlassClassification-GitHubLabeler/README.md
@@ -2,7 +2,7 @@
| ML.NET version | API type | Status | App Type | Data sources | Scenario | ML Task | Algorithms |
|----------------|-------------------|-------------------------------|-------------|-----------|---------------------|---------------------------|-----------------------------|
-| v1.2.0 | Dynamic API | Up-to-date | Console app | .csv file and GitHub issues | Issues classification | Multi-class classification | SDCA multi-class classifier, AveragedPerceptronTrainer |
+| v1.1.0 | Dynamic API | Up-to-date | Console app | .csv file and GitHub issues | Issues classification | Multi-class classification | SDCA multi-class classifier, AveragedPerceptronTrainer |
This is a simple prototype application to demonstrate how to use [ML.NET](https://www.nuget.org/packages/Microsoft.ML/) APIs. The main focus is on creating, training, and using ML (Machine Learning) model that is implemented in Predictor.cs class.
diff --git a/samples/csharp/end-to-end-apps/Recommendation-MovieRecommender/MovieRecommender/movierecommender/MovieRecommender.csproj b/samples/csharp/end-to-end-apps/Recommendation-MovieRecommender/MovieRecommender/movierecommender/MovieRecommender.csproj
index 7a76fffeb..34bb09e06 100644
--- a/samples/csharp/end-to-end-apps/Recommendation-MovieRecommender/MovieRecommender/movierecommender/MovieRecommender.csproj
+++ b/samples/csharp/end-to-end-apps/Recommendation-MovieRecommender/MovieRecommender/movierecommender/MovieRecommender.csproj
@@ -10,7 +10,7 @@
-
+
diff --git a/samples/csharp/end-to-end-apps/Recommendation-MovieRecommender/README.md b/samples/csharp/end-to-end-apps/Recommendation-MovieRecommender/README.md
index 58bc759b8..a26cfacd9 100644
--- a/samples/csharp/end-to-end-apps/Recommendation-MovieRecommender/README.md
+++ b/samples/csharp/end-to-end-apps/Recommendation-MovieRecommender/README.md
@@ -2,7 +2,7 @@
| ML.NET version | API type | Status | App Type | Data sources | Scenario | ML Task | Algorithms |
|----------------|-------------------|-------------------------------|-------------|-----------|---------------------|---------------------------|-----------------------------|
-|v1.2.0 | Dynamic API | up-to-date | End-End app | .csv | Movie Recommendation | Recommendation | Field Aware Factorization Machines |
+|v1.1.0 | Dynamic API | up-to-date | End-End app | .csv | Movie Recommendation | Recommendation | Field Aware Factorization Machines |
![Alt Text](https://github.com/dotnet/machinelearning-samples/blob/master/samples/csharp/end-to-end-apps/Recommendation-MovieRecommender/MovieRecommender/movierecommender/wwwroot/images/movierecommender.gif)
diff --git a/samples/csharp/end-to-end-apps/Regression-SalesForecast/README.md b/samples/csharp/end-to-end-apps/Regression-SalesForecast/README.md
index 5f0140f25..21c2e4a8b 100644
--- a/samples/csharp/end-to-end-apps/Regression-SalesForecast/README.md
+++ b/samples/csharp/end-to-end-apps/Regression-SalesForecast/README.md
@@ -2,7 +2,7 @@
| ML.NET version | API type | Status | App Type | Data type | Scenario | ML Task | Algorithms |
|----------------|-------------------|-------------------------------|-------------|-----------|---------------------|---------------------------|-----------------------------|
-| v1.2.0 | Dynamic API | Up-to-date | ASP.NET Core web app and Console app | SQL Server and .csv files | Sales forecast | Regression | FastTreeTweedie Regression |
+| v1.1.0 | Dynamic API | Up-to-date | ASP.NET Core web app and Console app | SQL Server and .csv files | Sales forecast | Regression | FastTreeTweedie Regression |
eShopDashboardML is a web app with Sales Forecast predictions (per product and per country) using [Microsoft Machine Learning .NET (ML.NET)](https://github.com/dotnet/machinelearning).
diff --git a/samples/csharp/end-to-end-apps/Regression-SalesForecast/src/eShopDashboard/eShopDashboard.csproj b/samples/csharp/end-to-end-apps/Regression-SalesForecast/src/eShopDashboard/eShopDashboard.csproj
index 8b8adeed6..6033c8f70 100644
--- a/samples/csharp/end-to-end-apps/Regression-SalesForecast/src/eShopDashboard/eShopDashboard.csproj
+++ b/samples/csharp/end-to-end-apps/Regression-SalesForecast/src/eShopDashboard/eShopDashboard.csproj
@@ -20,7 +20,7 @@
-
+
diff --git a/samples/csharp/end-to-end-apps/ScalableMLModelOnWebAPI-IntegrationPkg/README.md b/samples/csharp/end-to-end-apps/ScalableMLModelOnWebAPI-IntegrationPkg/README.md
index 0e99f3387..5a3a4f615 100644
--- a/samples/csharp/end-to-end-apps/ScalableMLModelOnWebAPI-IntegrationPkg/README.md
+++ b/samples/csharp/end-to-end-apps/ScalableMLModelOnWebAPI-IntegrationPkg/README.md
@@ -5,7 +5,7 @@
| ML.NET version | Status | App Type | Data type | Scenario | ML Task | Algorithms |
|----------------|-------------------------------|-------------|-----------|---------------------|---------------------------|-----------------------------|
-| v1.2.0 | Up-to-date | ASP.NET Core 2.2 WebAPI | Single data sample | Sentiment Analysis | Binary classification | Linear Classification |
+| v1.1.0 | Up-to-date | ASP.NET Core 2.2 WebAPI | Single data sample | Sentiment Analysis | Binary classification | Linear Classification |
**This posts explains how to optimize your code when running an ML.NET model on an ASP.NET Core WebAPI service.** The code would be very similar when running it on an ASP.NET Core MVC or Razor web app, too.
diff --git a/samples/csharp/end-to-end-apps/ScalableMLModelOnWebAPI-IntegrationPkg/src/Scalable.WebAPI/Scalable.WebAPI.csproj b/samples/csharp/end-to-end-apps/ScalableMLModelOnWebAPI-IntegrationPkg/src/Scalable.WebAPI/Scalable.WebAPI.csproj
index 6135ddc26..056d20b77 100644
--- a/samples/csharp/end-to-end-apps/ScalableMLModelOnWebAPI-IntegrationPkg/src/Scalable.WebAPI/Scalable.WebAPI.csproj
+++ b/samples/csharp/end-to-end-apps/ScalableMLModelOnWebAPI-IntegrationPkg/src/Scalable.WebAPI/Scalable.WebAPI.csproj
@@ -6,7 +6,7 @@
-
+
diff --git a/samples/csharp/end-to-end-apps/ScalableMLModelOnWebAPI/README.md b/samples/csharp/end-to-end-apps/ScalableMLModelOnWebAPI/README.md
index 8a222844d..fbdd63b54 100644
--- a/samples/csharp/end-to-end-apps/ScalableMLModelOnWebAPI/README.md
+++ b/samples/csharp/end-to-end-apps/ScalableMLModelOnWebAPI/README.md
@@ -5,7 +5,7 @@
| ML.NET version | Status | App Type | Data type | Scenario | ML Task | Algorithms |
|----------------|-------------------------------|-------------|-----------|---------------------|---------------------------|-----------------------------|
-| v1.2.0 | Up-to-date | ASP.NET Core 2.2 WebAPI | Single data sample | Sentiment Analysis | Binary classification | Linear Classification |
+| v1.0.0 | Up-to-date | ASP.NET Core 2.2 WebAPI | Single data sample | Sentiment Analysis | Binary classification | Linear Classification |
**This posts explains how to optimize your code when running an ML.NET model on an ASP.NET Core WebAPI service.** The code would be very similar when running it on an ASP.NET Core MVC or Razor web app, too.
diff --git a/samples/csharp/end-to-end-apps/ScalableSentimentAnalysisBlazorWebApp/BlazorSentimentAnalysis.Server/BlazorSentimentAnalysis.Server.csproj b/samples/csharp/end-to-end-apps/ScalableSentimentAnalysisBlazorWebApp/BlazorSentimentAnalysis.Server/BlazorSentimentAnalysis.Server.csproj
index ed00e7e4c..ba53b2a56 100644
--- a/samples/csharp/end-to-end-apps/ScalableSentimentAnalysisBlazorWebApp/BlazorSentimentAnalysis.Server/BlazorSentimentAnalysis.Server.csproj
+++ b/samples/csharp/end-to-end-apps/ScalableSentimentAnalysisBlazorWebApp/BlazorSentimentAnalysis.Server/BlazorSentimentAnalysis.Server.csproj
@@ -12,8 +12,8 @@
-
-
+
+
diff --git a/samples/csharp/end-to-end-apps/ScalableSentimentAnalysisBlazorWebApp/README.md b/samples/csharp/end-to-end-apps/ScalableSentimentAnalysisBlazorWebApp/README.md
index df86c0850..5ad058777 100644
--- a/samples/csharp/end-to-end-apps/ScalableSentimentAnalysisBlazorWebApp/README.md
+++ b/samples/csharp/end-to-end-apps/ScalableSentimentAnalysisBlazorWebApp/README.md
@@ -5,7 +5,7 @@
| ML.NET version | Status | App Type | Data type | Scenario | ML Task | Algorithms |
|----------------|-------------------------------|-------------|-----------|---------------------|---------------------------|-----------------------------|
-| v1.2.0 | Up-to-date | Blazor / ASP.NET Core 3.0 Preview 6 | Single data sample | Sentiment Analysis | Binary classification | Linear Classification |
+| v1.1.0 | Up-to-date | Blazor / ASP.NET Core 3.0 Preview 6 | Single data sample | Sentiment Analysis | Binary classification | Linear Classification |
# Goal
diff --git a/samples/csharp/getting-started/AnomalyDetection_PowerMeterReadings/README.md b/samples/csharp/getting-started/AnomalyDetection_PowerMeterReadings/README.md
index 155e518f9..aef3b2c1e 100644
--- a/samples/csharp/getting-started/AnomalyDetection_PowerMeterReadings/README.md
+++ b/samples/csharp/getting-started/AnomalyDetection_PowerMeterReadings/README.md
@@ -2,7 +2,7 @@
| ML.NET version | API type | Status | App Type | Data type | Scenario | ML Task | Algorithms |
|----------------|-------------------|-------------------------------|-------------|-----------|---------------------|---------------------------|-----------------------------|
-| v1.2.0 | Dynamic API | Up-to-date | Console app | .csv files | Power Meter Anomaly Detection | Time Series- Anomaly Detection | SsaSpikeDetection |
+| v1.1.0 | Dynamic API | Up-to-date | Console app | .csv files | Power Meter Anomaly Detection | Time Series- Anomaly Detection | SsaSpikeDetection |
In this sample, you'll see how to use [ML.NET](https://www.microsoft.com/net/learn/apps/machine-learning-and-ai/ml-dotnet) to detect anomalies in time series data.
diff --git a/samples/csharp/getting-started/AnomalyDetection_Sales/README.md b/samples/csharp/getting-started/AnomalyDetection_Sales/README.md
index 692af3579..d907a42f5 100644
--- a/samples/csharp/getting-started/AnomalyDetection_Sales/README.md
+++ b/samples/csharp/getting-started/AnomalyDetection_Sales/README.md
@@ -2,7 +2,7 @@
| ML.NET version | API type | Status | App Type | Data type | Scenario | ML Task | Algorithms |
|----------------|-------------------|-------------------------------|-------------|-----------|---------------------|---------------------------|-----------------------------|
-| v1.2.0 | Dynamic API | Up-to-date | Console app | .csv files | Product Sales Spike Detection| Time Series - Anomaly Detection | IID Spike Detection and IID Change point Detection |
+| v1.1.0 | Dynamic API | Up-to-date | Console app | .csv files | Product Sales Spike Detection| Time Series - Anomaly Detection | IID Spike Detection and IID Change point Detection |
In this introductory sample, you'll see how to use [ML.NET](https://www.microsoft.com/net/learn/apps/machine-learning-and-ai/ml-dotnet) to detect **spikes** and **change points** in Product sales. In the world of machine learning, this type of task is called TimeSeries Anomaly Detection.
diff --git a/samples/csharp/getting-started/BinaryClassification_CreditCardFraudDetection/Readme.md b/samples/csharp/getting-started/BinaryClassification_CreditCardFraudDetection/Readme.md
index aee51226b..0e37d7887 100644
--- a/samples/csharp/getting-started/BinaryClassification_CreditCardFraudDetection/Readme.md
+++ b/samples/csharp/getting-started/BinaryClassification_CreditCardFraudDetection/Readme.md
@@ -2,7 +2,7 @@
| ML.NET version | API type | Status | App Type | Data type | Scenario | ML Task | Algorithms |
|----------------|-------------------|-------------------------------|-------------|-----------|---------------------|---------------------------|-----------------------------|
-| v1.2.0 | Dynamic API | Up-to-date | Two console apps | .csv file | Fraud Detection | Two-class classification | FastTree Binary Classification |
+| v1.1.0 | Dynamic API | Up-to-date | Two console apps | .csv file | Fraud Detection | Two-class classification | FastTree Binary Classification |
In this introductory sample, you'll see how to use ML.NET to predict a credit card fraud. In the world of machine learning, this type of prediction is known as binary classification.
diff --git a/samples/csharp/getting-started/BinaryClassification_HeartDiseaseDetection/README.md b/samples/csharp/getting-started/BinaryClassification_HeartDiseaseDetection/README.md
index d7764aa4e..3c92f0282 100644
--- a/samples/csharp/getting-started/BinaryClassification_HeartDiseaseDetection/README.md
+++ b/samples/csharp/getting-started/BinaryClassification_HeartDiseaseDetection/README.md
@@ -2,7 +2,7 @@
| ML.NET version | API type | Status | App Type | Data type | Scenario | ML Task | Algorithms |
|----------------|-------------------|-------------------------------|-------------|-----------|---------------------|---------------------------|-----------------------------|
-| v1.2.0 | Dynamic API | Up-to-date | Console app | .txt files | Heart disease classification | Binary classification | FastTree |
+| v1.1.0 | Dynamic API | Up-to-date | Console app | .txt files | Heart disease classification | Binary classification | FastTree |
In this introductory sample, you'll see how to use [ML.NET](https://www.microsoft.com/net/learn/apps/machine-learning-and-ai/ml-dotnet) to predict type of heart disease. In the world of machine learning, this type of prediction is known as **binary classification**.
diff --git a/samples/csharp/getting-started/BinaryClassification_SentimentAnalysis/README.md b/samples/csharp/getting-started/BinaryClassification_SentimentAnalysis/README.md
index 2d8204ffa..0850275b2 100644
--- a/samples/csharp/getting-started/BinaryClassification_SentimentAnalysis/README.md
+++ b/samples/csharp/getting-started/BinaryClassification_SentimentAnalysis/README.md
@@ -2,7 +2,7 @@
| ML.NET version | API type | Status | App Type | Data type | Scenario | ML Task | Algorithms |
|----------------|-------------------|-------------------------------|-------------|-----------|---------------------|---------------------------|-----------------------------|
-| v1.2.0 | Dynamic API | up-to-date | Console app | .tsv files | Sentiment Analysis | Two-class classification | Linear Classification |
+| v1.1.0 | Dynamic API | up-to-date | Console app | .tsv files | Sentiment Analysis | Two-class classification | Linear Classification |
In this introductory sample, you'll see how to use [ML.NET](https://www.microsoft.com/net/learn/apps/machine-learning-and-ai/ml-dotnet) to predict a sentiment (positive or negative) for customer reviews. In the world of machine learning, this type of prediction is known as **binary classification**.
diff --git a/samples/csharp/getting-started/BinaryClassification_SpamDetection/README.md b/samples/csharp/getting-started/BinaryClassification_SpamDetection/README.md
index 4d2e2a0fe..98bd0cdc1 100644
--- a/samples/csharp/getting-started/BinaryClassification_SpamDetection/README.md
+++ b/samples/csharp/getting-started/BinaryClassification_SpamDetection/README.md
@@ -2,7 +2,7 @@
| ML.NET version | API type | Status | App Type | Data type | Scenario | ML Task | Algorithms |
|----------------|-------------------|-------------------------------|-------------|-----------|---------------------|---------------------------|-----------------------------|
-| v1.2.0 | Dynamic API | Might need to update project structure to match template | Console app | .tsv files | Spam detection | Two-class classification | Averaged Perceptron (linear learner) |
+| v1.1.0 | Dynamic API | Might need to update project structure to match template | Console app | .tsv files | Spam detection | Two-class classification | Averaged Perceptron (linear learner) |
In this sample, you'll see how to use [ML.NET](https://www.microsoft.com/net/learn/apps/machine-learning-and-ai/ml-dotnet) to predict whether a text message is spam. In the world of machine learning, this type of prediction is known as **binary classification**.
diff --git a/samples/csharp/getting-started/Clustering_CustomerSegmentation/README.md b/samples/csharp/getting-started/Clustering_CustomerSegmentation/README.md
index d6ca4e597..de0906270 100644
--- a/samples/csharp/getting-started/Clustering_CustomerSegmentation/README.md
+++ b/samples/csharp/getting-started/Clustering_CustomerSegmentation/README.md
@@ -2,7 +2,7 @@
| ML.NET version | API type | Status | App Type | Data type | Scenario | ML Task | Algorithms |
|----------------|-------------------|-------------------------------|-------------|-----------|---------------------|---------------------------|-----------------------------|
-| v1.2.0 | Dynamic API | Up-to-date | Console app | .csv files | Customer segmentation | Clustering | K-means++ |
+| v1.1.0 | Dynamic API | Up-to-date | Console app | .csv files | Customer segmentation | Clustering | K-means++ |
## Problem
diff --git a/samples/csharp/getting-started/Clustering_Iris/READMe.md b/samples/csharp/getting-started/Clustering_Iris/READMe.md
index 86a27a416..4e333f453 100644
--- a/samples/csharp/getting-started/Clustering_Iris/READMe.md
+++ b/samples/csharp/getting-started/Clustering_Iris/READMe.md
@@ -2,7 +2,7 @@
| ML.NET version | API type | Status | App Type | Data type | Scenario | ML Task | Algorithms |
|----------------|-------------------|-------------------------------|-------------|-----------|---------------------|---------------------------|-----------------------------|
-| v1.2.0 | Dynamic API | Up-to-date | Console app | .txt file | Clustering Iris flowers | Clustering | K-means++ |
+| v1.1.0 | Dynamic API | Up-to-date | Console app | .txt file | Clustering Iris flowers | Clustering | K-means++ |
In this introductory sample, you'll see how to use [ML.NET](https://www.microsoft.com/net/learn/apps/machine-learning-and-ai/ml-dotnet) to divide iris flowers into different groups that correspond to different types of iris. In the world of machine learning, this task is known as **clustering**.
diff --git a/samples/csharp/getting-started/DatabaseIntegration/DatabaseIntegration/DatabaseIntegration.csproj b/samples/csharp/getting-started/DatabaseIntegration/DatabaseIntegration/DatabaseIntegration.csproj
index b1ca9e367..9e240ebc3 100644
--- a/samples/csharp/getting-started/DatabaseIntegration/DatabaseIntegration/DatabaseIntegration.csproj
+++ b/samples/csharp/getting-started/DatabaseIntegration/DatabaseIntegration/DatabaseIntegration.csproj
@@ -8,9 +8,9 @@
-
-
-
+
+
+
diff --git a/samples/csharp/getting-started/DeepLearning_ImageClassification_TensorFlow/README.md b/samples/csharp/getting-started/DeepLearning_ImageClassification_TensorFlow/README.md
index 90e4fbda8..a675fbfcb 100644
--- a/samples/csharp/getting-started/DeepLearning_ImageClassification_TensorFlow/README.md
+++ b/samples/csharp/getting-started/DeepLearning_ImageClassification_TensorFlow/README.md
@@ -2,7 +2,7 @@
| ML.NET version | API type | Status | App Type | Data type | Scenario | ML Task | Algorithms |
|----------------|-------------------|-------------------------------|-------------|-----------|---------------------|---------------------------|-----------------------------|
-| v1.2.0 | Dynamic API | up-to-date | Console app | Images and text labels | Images classification | TensorFlow Inception5h | DeepLearning model |
+| v1.1.0 | Dynamic API | up-to-date | Console app | Images and text labels | Images classification | TensorFlow Inception5h | DeepLearning model |
## Problem
diff --git a/samples/csharp/getting-started/DeepLearning_ObjectDetection_Onnx/ObjectDetectionConsoleApp/ObjectDetection.csproj b/samples/csharp/getting-started/DeepLearning_ObjectDetection_Onnx/ObjectDetectionConsoleApp/ObjectDetection.csproj
index 6911551c3..055d99dc5 100644
--- a/samples/csharp/getting-started/DeepLearning_ObjectDetection_Onnx/ObjectDetectionConsoleApp/ObjectDetection.csproj
+++ b/samples/csharp/getting-started/DeepLearning_ObjectDetection_Onnx/ObjectDetectionConsoleApp/ObjectDetection.csproj
@@ -30,7 +30,7 @@
-
+
diff --git a/samples/csharp/getting-started/DeepLearning_ObjectDetection_Onnx/README.md b/samples/csharp/getting-started/DeepLearning_ObjectDetection_Onnx/README.md
index 893ee5839..978839712 100644
--- a/samples/csharp/getting-started/DeepLearning_ObjectDetection_Onnx/README.md
+++ b/samples/csharp/getting-started/DeepLearning_ObjectDetection_Onnx/README.md
@@ -2,7 +2,7 @@
| ML.NET version | API type | Status | App Type | Data type | Scenario | ML Task | Algorithms |
|----------------|-------------------|-------------------------------|-------------|-----------|---------------------|---------------------------|-----------------------------|
-| v1.2.0 | Dynamic API | Up-to-date | Console app | image files | Object Detection | Deep Learning | Tiny Yolo2 ONNX model |
+| v1.1.0 | Dynamic API | Up-to-date | Console app | image files | Object Detection | Deep Learning | Tiny Yolo2 ONNX model |
## Problem
Object detection is one of the classical problems in computer vision: Recognize what the objects are inside a given image and also where they are in the image. For these cases, you can either use pre-trained models or train your own model to classify images specific to your custom domain.
diff --git a/samples/csharp/getting-started/DeepLearning_TensorFlowEstimator/README.md b/samples/csharp/getting-started/DeepLearning_TensorFlowEstimator/README.md
index ed5d14b71..a6cabbf50 100644
--- a/samples/csharp/getting-started/DeepLearning_TensorFlowEstimator/README.md
+++ b/samples/csharp/getting-started/DeepLearning_TensorFlowEstimator/README.md
@@ -2,7 +2,7 @@
| ML.NET version | API type | Status | App Type | Data type | Scenario | ML Task | Algorithms |
|----------------|-------------------|-------------------------------|-------------|-----------|---------------------|---------------------------|-----------------------------|
-| v1.2.0 | Dynamic API | Up-to-date | Console app | .tsv + image files | Image classification | featurization + classification | deep neural network + SDCA |
+| v1.1.0 | Dynamic API | Up-to-date | Console app | .tsv + image files | Image classification | featurization + classification | deep neural network + SDCA |
## Problem
Image classification is a common problem which has been solved quite a while using Machine Learning techniques. In this sample, we will review an approach that mixes new techniques (deep learning) and old school (LbfgsMaximumEntropy) techniques.
diff --git a/samples/csharp/getting-started/LargeDatasets/README.md b/samples/csharp/getting-started/LargeDatasets/README.md
index a9f04fbd1..4437aac57 100644
--- a/samples/csharp/getting-started/LargeDatasets/README.md
+++ b/samples/csharp/getting-started/LargeDatasets/README.md
@@ -2,7 +2,7 @@
| ML.NET version | API type | Status | App Type | Data type | Scenario | ML Task | Algorithms |
|----------------|-------------------|-------------------------------|-------------|-----------|---------------------|---------------------------|-----------------------------|
-| v1.2.0 | Dynamic API | Up-to-date | Console app | .txt files | Large datasets | Binary classification | FieldAwareFactorizationMachine |
+| v1.1.0 | Dynamic API | Up-to-date | Console app | .txt files | Large datasets | Binary classification | FieldAwareFactorizationMachine |
In this introductory sample, you'll see how to use [ML.NET](https://www.microsoft.com/net/learn/apps/machine-learning-and-ai/ml-dotnet) to deal with **large datasets containing millions of records and thousands/millions of features**. ML.Net API can handle upto **1TB** of data.
diff --git a/samples/csharp/getting-started/MatrixFactorization_MovieRecommendation/README.md b/samples/csharp/getting-started/MatrixFactorization_MovieRecommendation/README.md
index d8f52bb99..dbc3da680 100644
--- a/samples/csharp/getting-started/MatrixFactorization_MovieRecommendation/README.md
+++ b/samples/csharp/getting-started/MatrixFactorization_MovieRecommendation/README.md
@@ -2,7 +2,7 @@
| ML.NET version | API type | Status | App Type | Data type | Scenario | ML Task | Algorithms |
|----------------|-------------------|-------------------------------|-------------|-----------|---------------------|---------------------------|-----------------------------|
-| v1.2.0 | Dynamic API | Up-to-date | Console app | .csv files | Recommendation | Matrix Factorization | MatrixFactorizationTrainer|
+| v1.1.0 | Dynamic API | Up-to-date | Console app | .csv files | Recommendation | Matrix Factorization | MatrixFactorizationTrainer|
In this sample, you can see how to use ML.NET to build a movie recommendation engine.
diff --git a/samples/csharp/getting-started/MatrixFactorization_ProductRecommendation/Readme.md b/samples/csharp/getting-started/MatrixFactorization_ProductRecommendation/Readme.md
index b1aceb000..e70b3b97d 100644
--- a/samples/csharp/getting-started/MatrixFactorization_ProductRecommendation/Readme.md
+++ b/samples/csharp/getting-started/MatrixFactorization_ProductRecommendation/Readme.md
@@ -2,7 +2,7 @@
| ML.NET version | API type | Status | App Type | Data type | Scenario | ML Task | Algorithms |
|----------------|-------------------|-------------------------------|-------------|-----------|---------------------|---------------------------|-----------------------------|
-|v1.2.0 | Dynamic API | Up-to-date | Console app | .txt files | Recommendation | Matrix Factorization | MatrixFactorizationTrainer (One Class)|
+|v1.1.0 | Dynamic API | Up-to-date | Console app | .txt files | Recommendation | Matrix Factorization | MatrixFactorizationTrainer (One Class)|
In this sample, you can see how to use ML.NET to build a product recommendation scenario.
diff --git a/samples/csharp/getting-started/MulticlassClassification_Iris/README.md b/samples/csharp/getting-started/MulticlassClassification_Iris/README.md
index a2c51f8cf..60e05484a 100644
--- a/samples/csharp/getting-started/MulticlassClassification_Iris/README.md
+++ b/samples/csharp/getting-started/MulticlassClassification_Iris/README.md
@@ -2,7 +2,7 @@
| ML.NET version | API type | Status | App Type | Data type | Scenario | ML Task | Algorithms |
|----------------|-------------------|-------------------------------|-------------|-----------|---------------------|---------------------------|-----------------------------|
-| v1.2.0 | Dynamic API | Up-to-date | Console app | .txt files | Iris flowers classification | Multi-class classification | Sdca Multi-class |
+| v1.1.0 | Dynamic API | Up-to-date | Console app | .txt files | Iris flowers classification | Multi-class classification | Sdca Multi-class |
In this introductory sample, you'll see how to use [ML.NET](https://www.microsoft.com/net/learn/apps/machine-learning-and-ai/ml-dotnet) to predict the type of iris flower. In the world of machine learning, this type of prediction is known as **multiclass classification**.
diff --git a/samples/csharp/getting-started/MulticlassClassification_MNIST/README.md b/samples/csharp/getting-started/MulticlassClassification_MNIST/README.md
index 20e6d7c4f..c94bb66d2 100644
--- a/samples/csharp/getting-started/MulticlassClassification_MNIST/README.md
+++ b/samples/csharp/getting-started/MulticlassClassification_MNIST/README.md
@@ -2,7 +2,7 @@
| ML.NET version | API type | Status | App Type | Data type | Scenario | ML Task | Algorithms |
|----------------|-------------------|-------------------------------|-------------|-----------|---------------------|---------------------------|-----------------------------|
-| v1.2.0 | Dynamic API | Up-to-date | Console app | .csv files | MNIST classification | Multi-class classification | Sdca Multi-class |
+| v1.1.0 | Dynamic API | Up-to-date | Console app | .csv files | MNIST classification | Multi-class classification | Sdca Multi-class |
In this introductory sample, you'll see how to use [ML.NET](https://www.microsoft.com/net/learn/apps/machine-learning-and-ai/ml-dotnet) to classify handwritten digits from 0 to 9 using the MNIST dataset. This is a **multiclass classification** problem that we will solve using SDCA (Stochastic Dual Coordinate Ascent) algorithm.
diff --git a/samples/csharp/getting-started/Ranking_Web/README.md b/samples/csharp/getting-started/Ranking_Web/README.md
index c2d1dcd00..3d0656d40 100644
--- a/samples/csharp/getting-started/Ranking_Web/README.md
+++ b/samples/csharp/getting-started/Ranking_Web/README.md
@@ -2,7 +2,7 @@
| ML.NET version | API type | Status | App Type | Data type | Scenario | ML Task | Algorithms |
|----------------|-------------------|-------------------------------|-------------|-----------|---------------------|---------------------------|-----------------------------|
-| v1.2.0 | Dynamic API | Up-to-date | Console app | .csv file | Ranking search engine results | Ranking | LightGBM |
+| v1.1.0 | Dynamic API | Up-to-date | Console app | .csv file | Ranking search engine results | Ranking | LightGBM |
This introductory sample shows how to use ML.NET to predict the the best order to display search engine results. In the world of machine learning, this type of prediction is known as ranking.
diff --git a/samples/csharp/getting-started/Ranking_Web/WebRanking/WebRanking.csproj b/samples/csharp/getting-started/Ranking_Web/WebRanking/WebRanking.csproj
index b01a0bb30..fbca6af6c 100644
--- a/samples/csharp/getting-started/Ranking_Web/WebRanking/WebRanking.csproj
+++ b/samples/csharp/getting-started/Ranking_Web/WebRanking/WebRanking.csproj
@@ -6,8 +6,8 @@
-
-
+
+
diff --git a/samples/csharp/getting-started/Regression_BikeSharingDemand/README.md b/samples/csharp/getting-started/Regression_BikeSharingDemand/README.md
index cb59b277e..7f20a1654 100644
--- a/samples/csharp/getting-started/Regression_BikeSharingDemand/README.md
+++ b/samples/csharp/getting-started/Regression_BikeSharingDemand/README.md
@@ -2,7 +2,7 @@
| ML.NET version | API type | Status | App Type | Data type | Scenario | ML Task | Algorithms |
|----------------|-------------------|-------------------------------|-------------|-----------|---------------------|---------------------------|-----------------------------|
-| v1.2.0 | Dynamic API | Up-to-date | Console app | .csv files | Demand prediction | Regression | Fast Tree regressor compared to additional regression algorithms|
+| v1.1.0 | Dynamic API | Up-to-date | Console app | .csv files | Demand prediction | Regression | Fast Tree regressor compared to additional regression algorithms|
In this sample, you can see how to use ML.NET to predict the demand of bikes. Since you are trying to predict specific numeric values based on past observed data, in machine learning this type of method for prediction is known as regression.
diff --git a/samples/csharp/getting-started/Regression_TaxiFarePrediction/README.md b/samples/csharp/getting-started/Regression_TaxiFarePrediction/README.md
index 7d0bc85ab..1962efddd 100644
--- a/samples/csharp/getting-started/Regression_TaxiFarePrediction/README.md
+++ b/samples/csharp/getting-started/Regression_TaxiFarePrediction/README.md
@@ -2,7 +2,7 @@
| ML.NET version | API type | Status | App Type | Data type | Scenario | ML Task | Algorithms |
|----------------|-------------------|-------------------------------|-------------|-----------|---------------------|---------------------------|-----------------------------|
-| v1.2.0 | Dynamic API | Up-to-date | Console app | .csv files | Price prediction | Regression | Sdca Regression |
+| v1.1.0 | Dynamic API | Up-to-date | Console app | .csv files | Price prediction | Regression | Sdca Regression |
In this introductory sample, you'll see how to use [ML.NET](https://www.microsoft.com/net/learn/apps/machine-learning-and-ai/ml-dotnet) to predict taxi fares. In the world of machine learning, this type of prediction is known as **regression**.
diff --git a/samples/csharp/v1.2.0-All-Samples.sln b/samples/csharp/v1.0.0-All-Samples.sln
similarity index 95%
rename from samples/csharp/v1.2.0-All-Samples.sln
rename to samples/csharp/v1.0.0-All-Samples.sln
index 7d7f0d06d..44487c525 100644
--- a/samples/csharp/v1.2.0-All-Samples.sln
+++ b/samples/csharp/v1.0.0-All-Samples.sln
@@ -1,7 +1,7 @@
Microsoft Visual Studio Solution File, Format Version 12.00
-# Visual Studio Version 16
-VisualStudioVersion = 16.0.29009.5
+# Visual Studio 15
+VisualStudioVersion = 15.0.28307.705
MinimumVisualStudioVersion = 10.0.40219.1
Project("{2150E333-8FDC-42A3-9474-1A3956D46DE8}") = "BikeSharingDemand.Solution", "BikeSharingDemand.Solution", "{820E8AF2-A47D-4AB8-A4AF-5CDFF97EBCDF}"
EndProject
@@ -109,6 +109,10 @@ Project("{2150E333-8FDC-42A3-9474-1A3956D46DE8}") = "ScalableMLModelIntegrationP
EndProject
Project("{9A19103F-16F7-4668-BE54-9A1E7A4F7556}") = "Scalable.WebAPI", "end-to-end-apps\ScalableMLModelOnWebAPI-IntegrationPkg\src\Scalable.WebAPI\Scalable.WebAPI.csproj", "{E415AAE3-AFCD-439A-BB18-27C93C5D231C}"
EndProject
+Project("{9A19103F-16F7-4668-BE54-9A1E7A4F7556}") = "SpikeDetectionModelTrainer", "end-to-end-apps\AnomalyDetection-Sales\SpikeDetectionE2EApp\SpikeDetection.ModelTrainer\SpikeDetectionModelTrainer.csproj", "{BACF7028-6A68-421B-A7CA-F5A10D822ED4}"
+EndProject
+Project("{FAE04EC0-301F-11D3-BF4B-00C04F79EFBC}") = "SpikeDetection.WinForms", "end-to-end-apps\AnomalyDetection-Sales\SpikeDetectionE2EApp\SpikeDetection.WinForms\SpikeDetection.WinForms.csproj", "{8B051595-03CD-4026-91BB-403F90CF8526}"
+EndProject
Project("{9A19103F-16F7-4668-BE54-9A1E7A4F7556}") = "PowerAnomalyDetection", "getting-started\AnomalyDetection_PowerMeterReadings\PowerAnomalyDetection\PowerAnomalyDetection.csproj", "{AC2A1A3F-84F6-4453-8E65-9327B576C8E1}"
EndProject
Project("{9A19103F-16F7-4668-BE54-9A1E7A4F7556}") = "OnnxObjectDetectionE2EAPP", "end-to-end-apps\DeepLearning_ObjectDetection_Onnx\OnnxObjectDetectionE2EAPP\OnnxObjectDetectionE2EAPP.csproj", "{6093FC8F-F15C-41BD-B0E1-67524947EB45}"
@@ -369,6 +373,22 @@ Global
{E415AAE3-AFCD-439A-BB18-27C93C5D231C}.Release|Any CPU.Build.0 = Release|Any CPU
{E415AAE3-AFCD-439A-BB18-27C93C5D231C}.Release|x64.ActiveCfg = Release|Any CPU
{E415AAE3-AFCD-439A-BB18-27C93C5D231C}.Release|x64.Build.0 = Release|Any CPU
+ {BACF7028-6A68-421B-A7CA-F5A10D822ED4}.Debug|Any CPU.ActiveCfg = Debug|Any CPU
+ {BACF7028-6A68-421B-A7CA-F5A10D822ED4}.Debug|Any CPU.Build.0 = Debug|Any CPU
+ {BACF7028-6A68-421B-A7CA-F5A10D822ED4}.Debug|x64.ActiveCfg = Debug|Any CPU
+ {BACF7028-6A68-421B-A7CA-F5A10D822ED4}.Debug|x64.Build.0 = Debug|Any CPU
+ {BACF7028-6A68-421B-A7CA-F5A10D822ED4}.Release|Any CPU.ActiveCfg = Release|Any CPU
+ {BACF7028-6A68-421B-A7CA-F5A10D822ED4}.Release|Any CPU.Build.0 = Release|Any CPU
+ {BACF7028-6A68-421B-A7CA-F5A10D822ED4}.Release|x64.ActiveCfg = Release|Any CPU
+ {BACF7028-6A68-421B-A7CA-F5A10D822ED4}.Release|x64.Build.0 = Release|Any CPU
+ {8B051595-03CD-4026-91BB-403F90CF8526}.Debug|Any CPU.ActiveCfg = Debug|Any CPU
+ {8B051595-03CD-4026-91BB-403F90CF8526}.Debug|Any CPU.Build.0 = Debug|Any CPU
+ {8B051595-03CD-4026-91BB-403F90CF8526}.Debug|x64.ActiveCfg = Debug|x64
+ {8B051595-03CD-4026-91BB-403F90CF8526}.Debug|x64.Build.0 = Debug|x64
+ {8B051595-03CD-4026-91BB-403F90CF8526}.Release|Any CPU.ActiveCfg = Release|Any CPU
+ {8B051595-03CD-4026-91BB-403F90CF8526}.Release|Any CPU.Build.0 = Release|Any CPU
+ {8B051595-03CD-4026-91BB-403F90CF8526}.Release|x64.ActiveCfg = Release|x64
+ {8B051595-03CD-4026-91BB-403F90CF8526}.Release|x64.Build.0 = Release|x64
{AC2A1A3F-84F6-4453-8E65-9327B576C8E1}.Debug|Any CPU.ActiveCfg = Debug|Any CPU
{AC2A1A3F-84F6-4453-8E65-9327B576C8E1}.Debug|Any CPU.Build.0 = Debug|Any CPU
{AC2A1A3F-84F6-4453-8E65-9327B576C8E1}.Debug|x64.ActiveCfg = Debug|Any CPU
@@ -451,6 +471,8 @@ Global
{369C9044-8DDF-4E32-8B4E-BBFB583FA395} = {70958053-9A93-47D5-A944-2BA34E475618}
{212AEE3D-E008-4EC5-9DC4-EAF6A162A0ED} = {A56C7785-F74C-41F4-92C7-E98CB2287B90}
{E415AAE3-AFCD-439A-BB18-27C93C5D231C} = {6BCE0EE0-273A-4628-BD5E-45F456EEBC31}
+ {BACF7028-6A68-421B-A7CA-F5A10D822ED4} = {6D589303-EC5C-405C-B9F6-06FE3500FCCB}
+ {8B051595-03CD-4026-91BB-403F90CF8526} = {6D589303-EC5C-405C-B9F6-06FE3500FCCB}
{AC2A1A3F-84F6-4453-8E65-9327B576C8E1} = {417CA47F-36DE-4F6E-B53D-330D2B373ECF}
{6093FC8F-F15C-41BD-B0E1-67524947EB45} = {9F1B2D3E-F193-4D27-A1B8-7EEB16AC61B9}
{EA9E37C6-8C62-4370-A9CF-369D002B89B6} = {7C3A7DA5-CBEB-420F-B7AC-CDE34BE2D52E}
diff --git a/samples/csharp/v1.1.0-Samples.sln b/samples/csharp/v1.1.0-Samples.sln
deleted file mode 100644
index ec343e8fa..000000000
--- a/samples/csharp/v1.1.0-Samples.sln
+++ /dev/null
@@ -1,47 +0,0 @@
-
-Microsoft Visual Studio Solution File, Format Version 12.00
-# Visual Studio Version 16
-VisualStudioVersion = 16.0.29009.5
-MinimumVisualStudioVersion = 10.0.40219.1
-Project("{2150E333-8FDC-42A3-9474-1A3956D46DE8}") = "SpikeDetectionE2E.Solution", "SpikeDetectionE2E.Solution", "{6D589303-EC5C-405C-B9F6-06FE3500FCCB}"
-EndProject
-Project("{9A19103F-16F7-4668-BE54-9A1E7A4F7556}") = "SpikeDetectionModelTrainer", "end-to-end-apps\AnomalyDetection-Sales\SpikeDetectionE2EApp\SpikeDetection.ModelTrainer\SpikeDetectionModelTrainer.csproj", "{BACF7028-6A68-421B-A7CA-F5A10D822ED4}"
-EndProject
-Project("{FAE04EC0-301F-11D3-BF4B-00C04F79EFBC}") = "SpikeDetection.WinForms", "end-to-end-apps\AnomalyDetection-Sales\SpikeDetectionE2EApp\SpikeDetection.WinForms\SpikeDetection.WinForms.csproj", "{8B051595-03CD-4026-91BB-403F90CF8526}"
-EndProject
-Global
- GlobalSection(SolutionConfigurationPlatforms) = preSolution
- Debug|Any CPU = Debug|Any CPU
- Debug|x64 = Debug|x64
- Release|Any CPU = Release|Any CPU
- Release|x64 = Release|x64
- EndGlobalSection
- GlobalSection(ProjectConfigurationPlatforms) = postSolution
- {BACF7028-6A68-421B-A7CA-F5A10D822ED4}.Debug|Any CPU.ActiveCfg = Debug|Any CPU
- {BACF7028-6A68-421B-A7CA-F5A10D822ED4}.Debug|Any CPU.Build.0 = Debug|Any CPU
- {BACF7028-6A68-421B-A7CA-F5A10D822ED4}.Debug|x64.ActiveCfg = Debug|Any CPU
- {BACF7028-6A68-421B-A7CA-F5A10D822ED4}.Debug|x64.Build.0 = Debug|Any CPU
- {BACF7028-6A68-421B-A7CA-F5A10D822ED4}.Release|Any CPU.ActiveCfg = Release|Any CPU
- {BACF7028-6A68-421B-A7CA-F5A10D822ED4}.Release|Any CPU.Build.0 = Release|Any CPU
- {BACF7028-6A68-421B-A7CA-F5A10D822ED4}.Release|x64.ActiveCfg = Release|Any CPU
- {BACF7028-6A68-421B-A7CA-F5A10D822ED4}.Release|x64.Build.0 = Release|Any CPU
- {8B051595-03CD-4026-91BB-403F90CF8526}.Debug|Any CPU.ActiveCfg = Debug|Any CPU
- {8B051595-03CD-4026-91BB-403F90CF8526}.Debug|Any CPU.Build.0 = Debug|Any CPU
- {8B051595-03CD-4026-91BB-403F90CF8526}.Debug|x64.ActiveCfg = Debug|x64
- {8B051595-03CD-4026-91BB-403F90CF8526}.Debug|x64.Build.0 = Debug|x64
- {8B051595-03CD-4026-91BB-403F90CF8526}.Release|Any CPU.ActiveCfg = Release|Any CPU
- {8B051595-03CD-4026-91BB-403F90CF8526}.Release|Any CPU.Build.0 = Release|Any CPU
- {8B051595-03CD-4026-91BB-403F90CF8526}.Release|x64.ActiveCfg = Release|x64
- {8B051595-03CD-4026-91BB-403F90CF8526}.Release|x64.Build.0 = Release|x64
- EndGlobalSection
- GlobalSection(SolutionProperties) = preSolution
- HideSolutionNode = FALSE
- EndGlobalSection
- GlobalSection(NestedProjects) = preSolution
- {BACF7028-6A68-421B-A7CA-F5A10D822ED4} = {6D589303-EC5C-405C-B9F6-06FE3500FCCB}
- {8B051595-03CD-4026-91BB-403F90CF8526} = {6D589303-EC5C-405C-B9F6-06FE3500FCCB}
- EndGlobalSection
- GlobalSection(ExtensibilityGlobals) = postSolution
- SolutionGuid = {98369941-33DD-450C-A410-B9A91C8CDE91}
- EndGlobalSection
-EndGlobal
diff --git a/samples/fsharp/end-to-end-apps/MulticlassClassification-GitHubLabeler/README.md b/samples/fsharp/end-to-end-apps/MulticlassClassification-GitHubLabeler/README.md
index 0ce561b3e..a0de87efb 100644
--- a/samples/fsharp/end-to-end-apps/MulticlassClassification-GitHubLabeler/README.md
+++ b/samples/fsharp/end-to-end-apps/MulticlassClassification-GitHubLabeler/README.md
@@ -2,7 +2,7 @@
| ML.NET version | API type | Status | App Type | Data sources | Scenario | ML Task | Algorithms |
|----------------|-------------------|-------------------------------|-------------|-----------|---------------------|---------------------------|-----------------------------|
-| v1.2.0 | Dynamic API | Up-to-date | Console app | .csv file and GitHub issues | Issues classification | Multi-class classification | SDCA multi-class classifier |
+| v1.0.0-preview | Dynamic API | Up-to-date | Console app | .csv file and GitHub issues | Issues classification | Multi-class classification | SDCA multi-class classifier |
This is a simple prototype application to demonstrate how to use [ML.NET](https://www.nuget.org/packages/Microsoft.ML/) APIs. The main focus is on creating, training, and using ML (Machine Learning) model that is implemented in Predictor.cs class.
diff --git a/samples/fsharp/getting-started/BinaryClassification_CreditCardFraudDetection/Readme.md b/samples/fsharp/getting-started/BinaryClassification_CreditCardFraudDetection/Readme.md
index 4d9fc0fff..ff67ab998 100644
--- a/samples/fsharp/getting-started/BinaryClassification_CreditCardFraudDetection/Readme.md
+++ b/samples/fsharp/getting-started/BinaryClassification_CreditCardFraudDetection/Readme.md
@@ -2,7 +2,7 @@
| ML.NET version | API type | Status | App Type | Data type | Scenario | ML Task | Algorithms |
|----------------|-------------------|-------------------------------|-------------|-----------|---------------------|---------------------------|-----------------------------|
-| v1.2.0 | Dynamic API | Up-to-date | Two console apps | .csv file | Fraud Detection | Two-class classification | FastTree Binary Classification |
+| v1.0.0-preview | Dynamic API | Up-to-date | Two console apps | .csv file | Fraud Detection | Two-class classification | FastTree Binary Classification |
In this introductory sample, you'll see how to use ML.NET to predict a credit card fraud. In the world of machine learning, this type of prediction is known as binary classification.
diff --git a/samples/fsharp/getting-started/BinaryClassification_HeartDiseaseDetection/README.md b/samples/fsharp/getting-started/BinaryClassification_HeartDiseaseDetection/README.md
index fde8c20ea..248cad3ce 100644
--- a/samples/fsharp/getting-started/BinaryClassification_HeartDiseaseDetection/README.md
+++ b/samples/fsharp/getting-started/BinaryClassification_HeartDiseaseDetection/README.md
@@ -2,7 +2,7 @@
| ML.NET version | API type | Status | App Type | Data type | Scenario | ML Task | Algorithms |
|----------------|-------------------|-------------------------------|-------------|-----------|---------------------|---------------------------|-----------------------------|
-| v1.2.0 | Dynamic API | Up-to-date | Console app | .txt files | Heart disease classification | Binary classification | FastTree |
+| v1.0.0-preview | Dynamic API | Up-to-date | Console app | .txt files | Heart disease classification | Binary classification | FastTree |
In this introductory sample, you'll see how to use [ML.NET](https://www.microsoft.com/net/learn/apps/machine-learning-and-ai/ml-dotnet) to predict type of heart disease. In the world of machine learning, this type of prediction is known as **binary classification**.
diff --git a/samples/fsharp/getting-started/BinaryClassification_SentimentAnalysis/README.md b/samples/fsharp/getting-started/BinaryClassification_SentimentAnalysis/README.md
index 5312ccb8c..70f7f7808 100644
--- a/samples/fsharp/getting-started/BinaryClassification_SentimentAnalysis/README.md
+++ b/samples/fsharp/getting-started/BinaryClassification_SentimentAnalysis/README.md
@@ -2,7 +2,7 @@
| ML.NET version | API type | Status | App Type | Data type | Scenario | ML Task | Algorithms |
|----------------|-------------------|-------------------------------|-------------|-----------|---------------------|---------------------------|-----------------------------|
-| v1.2.0 | Dynamic API | README.md updated | Console app | .tsv files | Sentiment Analysis | Two-class classification | Linear Classification |
+| v1.0.0-preview | Dynamic API | README.md updated | Console app | .tsv files | Sentiment Analysis | Two-class classification | Linear Classification |
------------------------------------
diff --git a/samples/fsharp/getting-started/BinaryClassification_SpamDetection/README.md b/samples/fsharp/getting-started/BinaryClassification_SpamDetection/README.md
index c9008e72e..d028445ac 100644
--- a/samples/fsharp/getting-started/BinaryClassification_SpamDetection/README.md
+++ b/samples/fsharp/getting-started/BinaryClassification_SpamDetection/README.md
@@ -2,7 +2,7 @@
| ML.NET version | API type | Status | App Type | Data type | Scenario | ML Task | Algorithms |
|----------------|-------------------|-------------------------------|-------------|-----------|---------------------|---------------------------|-----------------------------|
-| v1.2.0 | Dynamic API | Up-to-date | Console app | .tsv files | Spam detection | Two-class classification | SDCA (linear learner) |
+| v1.0.0-preview | Dynamic API | Up-to-date | Console app | .tsv files | Spam detection | Two-class classification | SDCA (linear learner) |
In this sample, you'll see how to use [ML.NET](https://www.microsoft.com/net/learn/apps/machine-learning-and-ai/ml-dotnet) to predict whether a text message is spam. In the world of machine learning, this type of prediction is known as **binary classification**.
diff --git a/samples/fsharp/getting-started/Clustering_CustomerSegmentation/README.md b/samples/fsharp/getting-started/Clustering_CustomerSegmentation/README.md
index c8fd8cbce..72e7a5bf6 100644
--- a/samples/fsharp/getting-started/Clustering_CustomerSegmentation/README.md
+++ b/samples/fsharp/getting-started/Clustering_CustomerSegmentation/README.md
@@ -2,7 +2,7 @@
| ML.NET version | API type | Status | App Type | Data type | Scenario | ML Task | Algorithms |
|----------------|-------------------|-------------------------------|-------------|-----------|---------------------|---------------------------|-----------------------------|
-| v1.2.0 | Dynamic API | Up-to-date | Console app | .csv files | Customer segmentation | Clustering | K-means++ |
+| v1.0.0-preview | Dynamic API | Up-to-date | Console app | .csv files | Customer segmentation | Clustering | K-means++ |
## Problem
diff --git a/samples/fsharp/getting-started/Clustering_Iris/README.md b/samples/fsharp/getting-started/Clustering_Iris/README.md
index de93903f4..080a98779 100644
--- a/samples/fsharp/getting-started/Clustering_Iris/README.md
+++ b/samples/fsharp/getting-started/Clustering_Iris/README.md
@@ -2,7 +2,7 @@
| ML.NET version | API type | Status | App Type | Data type | Scenario | ML Task | Algorithms |
|----------------|-------------------|-------------------------------|-------------|-----------|---------------------|---------------------------|-----------------------------|
-| v1.2.0 | Dynamic API | Up-to-date | Console app | .txt file | Clustering Iris flowers | Clustering | K-means++ |
+| v1.0.0-preview | Dynamic API | Up-to-date | Console app | .txt file | Clustering Iris flowers | Clustering | K-means++ |
In this introductory sample, you'll see how to use [ML.NET](https://www.microsoft.com/net/learn/apps/machine-learning-and-ai/ml-dotnet) to divide iris flowers into different groups that correspond to different types of iris. In the world of machine learning, this task is known as **clustering**.
diff --git a/samples/fsharp/getting-started/DeepLearning_ImageClassification_TensorFlow/ImageClassification/ImageClassification.Score.fsproj b/samples/fsharp/getting-started/DeepLearning_ImageClassification_TensorFlow/ImageClassification/ImageClassification.Score.fsproj
index 682377336..96b41082b 100644
--- a/samples/fsharp/getting-started/DeepLearning_ImageClassification_TensorFlow/ImageClassification/ImageClassification.Score.fsproj
+++ b/samples/fsharp/getting-started/DeepLearning_ImageClassification_TensorFlow/ImageClassification/ImageClassification.Score.fsproj
@@ -12,7 +12,7 @@
-
+
diff --git a/samples/fsharp/getting-started/DeepLearning_ImageClassification_TensorFlow/README.md b/samples/fsharp/getting-started/DeepLearning_ImageClassification_TensorFlow/README.md
index 6d4205c1e..d495eeebd 100644
--- a/samples/fsharp/getting-started/DeepLearning_ImageClassification_TensorFlow/README.md
+++ b/samples/fsharp/getting-started/DeepLearning_ImageClassification_TensorFlow/README.md
@@ -2,7 +2,7 @@
| ML.NET version | API type | Status | App Type | Data type | Scenario | ML Task | Algorithms |
|----------------|-------------------|-------------------------------|-------------|-----------|---------------------|---------------------------|-----------------------------|
-| v1.2.0 | Dynamic API | up-to-date | Console app | Images and text labels | Images classification | TensorFlow Inceptionv3 | DeepLearning model |
+| v1.0.0-preview | Dynamic API | up-to-date | Console app | Images and text labels | Images classification | TensorFlow Inceptionv3 | DeepLearning model |
## Problem
diff --git a/samples/fsharp/getting-started/DeepLearning_ObjectDetection_Onnx/ObjectDetectionConsoleApp/ObjectDetection.fsproj b/samples/fsharp/getting-started/DeepLearning_ObjectDetection_Onnx/ObjectDetectionConsoleApp/ObjectDetection.fsproj
index 25d6774c1..2bebcd35c 100644
--- a/samples/fsharp/getting-started/DeepLearning_ObjectDetection_Onnx/ObjectDetectionConsoleApp/ObjectDetection.fsproj
+++ b/samples/fsharp/getting-started/DeepLearning_ObjectDetection_Onnx/ObjectDetectionConsoleApp/ObjectDetection.fsproj
@@ -12,7 +12,7 @@
-
+
diff --git a/samples/fsharp/getting-started/DeepLearning_ObjectDetection_Onnx/README.md b/samples/fsharp/getting-started/DeepLearning_ObjectDetection_Onnx/README.md
index 090660c1b..738a3b95a 100644
--- a/samples/fsharp/getting-started/DeepLearning_ObjectDetection_Onnx/README.md
+++ b/samples/fsharp/getting-started/DeepLearning_ObjectDetection_Onnx/README.md
@@ -2,7 +2,7 @@
| ML.NET version | API type | Status | App Type | Data type | Scenario | ML Task | Algorithms |
|----------------|-------------------|-------------------------------|-------------|-----------|---------------------|---------------------------|-----------------------------|
-| v1.2.0 | Dynamic API | Up-to-date | Console app | .tsv + image files | Object Detection | Deep Learning | Tiny Yolo2 ONNX model |
+| v1.0.0-preview | Dynamic API | Up-to-date | Console app | .tsv + image files | Object Detection | Deep Learning | Tiny Yolo2 ONNX model |
## Problem
Object detection is one of the classical problems in computer vision: Recognize what the objects are inside a given image and also where they are in the image. For these cases, you can either use pre-trained models or train your own model to classify images specific to your custom domain.
diff --git a/samples/fsharp/getting-started/DeepLearning_TensorFlowEstimator/ImageClassification.Predict/ImageClassification.Predict.fsproj b/samples/fsharp/getting-started/DeepLearning_TensorFlowEstimator/ImageClassification.Predict/ImageClassification.Predict.fsproj
index 682377336..96b41082b 100644
--- a/samples/fsharp/getting-started/DeepLearning_TensorFlowEstimator/ImageClassification.Predict/ImageClassification.Predict.fsproj
+++ b/samples/fsharp/getting-started/DeepLearning_TensorFlowEstimator/ImageClassification.Predict/ImageClassification.Predict.fsproj
@@ -12,7 +12,7 @@
-
+
diff --git a/samples/fsharp/getting-started/DeepLearning_TensorFlowEstimator/ImageClassification.Train/ImageClassification.Train.fsproj b/samples/fsharp/getting-started/DeepLearning_TensorFlowEstimator/ImageClassification.Train/ImageClassification.Train.fsproj
index 682377336..96b41082b 100644
--- a/samples/fsharp/getting-started/DeepLearning_TensorFlowEstimator/ImageClassification.Train/ImageClassification.Train.fsproj
+++ b/samples/fsharp/getting-started/DeepLearning_TensorFlowEstimator/ImageClassification.Train/ImageClassification.Train.fsproj
@@ -12,7 +12,7 @@
-
+
diff --git a/samples/fsharp/getting-started/DeepLearning_TensorFlowEstimator/README.md b/samples/fsharp/getting-started/DeepLearning_TensorFlowEstimator/README.md
index c71aa2c5b..6479e2b38 100644
--- a/samples/fsharp/getting-started/DeepLearning_TensorFlowEstimator/README.md
+++ b/samples/fsharp/getting-started/DeepLearning_TensorFlowEstimator/README.md
@@ -3,7 +3,7 @@
| ML.NET version | API type | Status | App Type | Data type | Scenario | ML Task | Algorithms |
|----------------|-------------------|-------------------------------|-------------|-----------|---------------------|---------------------------|-----------------------------|
-| v1.2.0 | Dynamic API | Up-to-date | Console app | .tsv + image files | Image classification | featurization + classification | deep neural network + SDCA |
+| v0.10 | Dynamic API | Up-to-date | Console app | .tsv + image files | Image classification | featurization + classification | deep neural network + SDCA |
## Problem
diff --git a/samples/fsharp/getting-started/MatrixFactorization_ProductRecommendation/ProductRecommender/ProductRecommender.fsproj b/samples/fsharp/getting-started/MatrixFactorization_ProductRecommendation/ProductRecommender/ProductRecommender.fsproj
index 269bcc749..87843327d 100644
--- a/samples/fsharp/getting-started/MatrixFactorization_ProductRecommendation/ProductRecommender/ProductRecommender.fsproj
+++ b/samples/fsharp/getting-started/MatrixFactorization_ProductRecommendation/ProductRecommender/ProductRecommender.fsproj
@@ -8,7 +8,7 @@
-
+
diff --git a/samples/fsharp/getting-started/MatrixFactorization_ProductRecommendation/Readme.md b/samples/fsharp/getting-started/MatrixFactorization_ProductRecommendation/Readme.md
index 7a2e6d6db..0c9ca2533 100644
--- a/samples/fsharp/getting-started/MatrixFactorization_ProductRecommendation/Readme.md
+++ b/samples/fsharp/getting-started/MatrixFactorization_ProductRecommendation/Readme.md
@@ -2,7 +2,7 @@
| ML.NET version | API type | Status | App Type | Data type | Scenario | ML Task | Algorithms |
|----------------|-------------------|-------------------------------|-------------|-----------|---------------------|---------------------------|-----------------------------|
-|v1.2.0 | Dynamic API | Up-to-date | Console app | .txt files | Recommendation | Matrix Factorization | MatrixFactorizationTrainer (One Class)|
+|v1.0.0-preview | Dynamic API | Up-to-date | Console app | .txt files | Recommendation | Matrix Factorization | MatrixFactorizationTrainer (One Class)|
In this sample, you can see how to use ML.NET to build a product recommendation scenario.
diff --git a/samples/fsharp/getting-started/MulticlassClassification_Iris/README.md b/samples/fsharp/getting-started/MulticlassClassification_Iris/README.md
index 2d90e90a8..f946958b9 100644
--- a/samples/fsharp/getting-started/MulticlassClassification_Iris/README.md
+++ b/samples/fsharp/getting-started/MulticlassClassification_Iris/README.md
@@ -2,7 +2,7 @@
| ML.NET version | API type | Status | App Type | Data type | Scenario | ML Task | Algorithms |
|----------------|-------------------|-------------------------------|-------------|-----------|---------------------|---------------------------|-----------------------------|
-| v1.2.0 | Dynamic API | Up-to-date | Console app | .txt files | Iris flowers classification | Multi-class classification | Sdca Multi-class |
+| v1.0.0-preview | Dynamic API | Up-to-date | Console app | .txt files | Iris flowers classification | Multi-class classification | Sdca Multi-class |
In this introductory sample, you'll see how to use [ML.NET](https://www.microsoft.com/net/learn/apps/machine-learning-and-ai/ml-dotnet) to predict the type of iris flower. In the world of machine learning, this type of prediction is known as **multiclass classification**.
diff --git a/samples/fsharp/getting-started/MulticlassClassification_mnist/README.md b/samples/fsharp/getting-started/MulticlassClassification_mnist/README.md
index 50a36a29c..77b11b7a3 100644
--- a/samples/fsharp/getting-started/MulticlassClassification_mnist/README.md
+++ b/samples/fsharp/getting-started/MulticlassClassification_mnist/README.md
@@ -2,7 +2,7 @@
| ML.NET version | API type | Status | App Type | Data type | Scenario | ML Task | Algorithms |
|----------------|-------------------|-------------------------------|-------------|-----------|---------------------|---------------------------|-----------------------------|
-| v1.2.0 | Dynamic API | Up-to-date | Console app | .csv files | MNIST classification | Multi-class classification | Sdca Multi-class |
+| v1.0.0-preview | Dynamic API | Up-to-date | Console app | .csv files | MNIST classification | Multi-class classification | Sdca Multi-class |
In this introductory sample, you'll see how to use [ML.NET](https://www.microsoft.com/net/learn/apps/machine-learning-and-ai/ml-dotnet) to classify handwritten digits from 0 to 9 using the MNIST dataset. This is a **multiclass classification** problem that we will solve using SDCA (Stochastic Dual Coordinate Ascent) algorithm.
diff --git a/samples/fsharp/getting-started/Regression_BikeSharingDemand/README.md b/samples/fsharp/getting-started/Regression_BikeSharingDemand/README.md
index 7f0891a88..80f19620e 100644
--- a/samples/fsharp/getting-started/Regression_BikeSharingDemand/README.md
+++ b/samples/fsharp/getting-started/Regression_BikeSharingDemand/README.md
@@ -2,7 +2,7 @@
| ML.NET version | API type | Status | App Type | Data type | Scenario | ML Task | Algorithms |
|----------------|-------------------|-------------------------------|-------------|-----------|---------------------|---------------------------|-----------------------------|
-| v1.2.0 | Dynamic API | Up-to-date | Console app | .csv files | Demand prediction | Regression | Fast Tree regressor compared to additional regression algorithms|
+| v1.0.0-preview | Dynamic API | Up-to-date | Console app | .csv files | Demand prediction | Regression | Fast Tree regressor compared to additional regression algorithms|
In this sample, you can see how to use ML.NET to predict the demand of bikes. Since you are trying to predict specific numeric values based on past observed data, in machine learning this type of method for prediction is known as regression.
diff --git a/samples/fsharp/getting-started/Regression_TaxiFarePrediction/README.md b/samples/fsharp/getting-started/Regression_TaxiFarePrediction/README.md
index bf4743fa4..154276c0b 100644
--- a/samples/fsharp/getting-started/Regression_TaxiFarePrediction/README.md
+++ b/samples/fsharp/getting-started/Regression_TaxiFarePrediction/README.md
@@ -2,7 +2,7 @@
| ML.NET version | API type | Status | App Type | Data type | Scenario | ML Task | Algorithms |
|----------------|-------------------|-------------------------------|-------------|-----------|---------------------|---------------------------|-----------------------------|
-| v1.2.0 | Dynamic API | Up-to-date | Console app | .csv files | Price prediction | Regression | Sdca Regression |
+| v1.0.0-preview | Dynamic API | Up-to-date | Console app | .csv files | Price prediction | Regression | Sdca Regression |
In this introductory sample, you'll see how to use [ML.NET](https://www.microsoft.com/net/learn/apps/machine-learning-and-ai/ml-dotnet) to predict taxi fares. In the world of machine learning, this type of prediction is known as **regression**.
diff --git a/samples/fsharp/getting-started/SpikeDetection_ShampooSales/README.md b/samples/fsharp/getting-started/SpikeDetection_ShampooSales/README.md
index d4cd14293..40bb6ac28 100644
--- a/samples/fsharp/getting-started/SpikeDetection_ShampooSales/README.md
+++ b/samples/fsharp/getting-started/SpikeDetection_ShampooSales/README.md
@@ -2,7 +2,7 @@
| ML.NET version | API type | Status | App Type | Data type | Scenario | ML Task | Algorithms |
|----------------|-------------------|-------------------------------|-------------|-----------|---------------------|---------------------------|-----------------------------|
-| v1.2.0 | Dynamic API | Up-to-date | Console app | .csv files | Shampoo sales Spike detection| Time Series - Anomaly Detection | IID Spike Detection and IID Change point Detection |
+| v1.0.0-preview | Dynamic API | Up-to-date | Console app | .csv files | Shampoo sales Spike detection| Time Series - Anomaly Detection | IID Spike Detection and IID Change point Detection |
In this introductory sample, you'll see how to use [ML.NET](https://www.microsoft.com/net/learn/apps/machine-learning-and-ai/ml-dotnet) to detect **spikes** and **Change points** in shampoo sales. In the world of machine learning, this type of task is called TimeSeries Anomaly Detection.
diff --git a/samples/fsharp/getting-started/SpikeDetection_ShampooSales/ShampooSales/ShampooSalesConsoleApp/ShampooSales.fsproj b/samples/fsharp/getting-started/SpikeDetection_ShampooSales/ShampooSales/ShampooSalesConsoleApp/ShampooSales.fsproj
index 72d6596b7..c88c9fe55 100644
--- a/samples/fsharp/getting-started/SpikeDetection_ShampooSales/ShampooSales/ShampooSalesConsoleApp/ShampooSales.fsproj
+++ b/samples/fsharp/getting-started/SpikeDetection_ShampooSales/ShampooSales/ShampooSalesConsoleApp/ShampooSales.fsproj
@@ -11,7 +11,7 @@
-
+
diff --git a/samples/fsharp/getting-started/TimeSeries_PowerAnomalyDetection/PowerAnomalyDetection/PowerAnomalyDetection.fsproj b/samples/fsharp/getting-started/TimeSeries_PowerAnomalyDetection/PowerAnomalyDetection/PowerAnomalyDetection.fsproj
index 72d6596b7..c88c9fe55 100644
--- a/samples/fsharp/getting-started/TimeSeries_PowerAnomalyDetection/PowerAnomalyDetection/PowerAnomalyDetection.fsproj
+++ b/samples/fsharp/getting-started/TimeSeries_PowerAnomalyDetection/PowerAnomalyDetection/PowerAnomalyDetection.fsproj
@@ -11,7 +11,7 @@
-
+
diff --git a/samples/fsharp/getting-started/TimeSeries_PowerAnomalyDetection/README.md b/samples/fsharp/getting-started/TimeSeries_PowerAnomalyDetection/README.md
index 27d752403..2f925ccdc 100644
--- a/samples/fsharp/getting-started/TimeSeries_PowerAnomalyDetection/README.md
+++ b/samples/fsharp/getting-started/TimeSeries_PowerAnomalyDetection/README.md
@@ -2,7 +2,7 @@
| ML.NET version | API type | Status | App Type | Data type | Scenario | ML Task | Algorithms |
|----------------|-------------------|-------------------------------|-------------|-----------|---------------------|---------------------------|-----------------------------|
-| v1.2.0 | Dynamic API | Up-to-date | Console app | .csv files | Power Meter Anomaly Detection | Time Series- Anomaly Detection | SsaSpikeDetection |
+| v1.0.0-preview | Dynamic API | Up-to-date | Console app | .csv files | Power Meter Anomaly Detection | Time Series- Anomaly Detection | SsaSpikeDetection |
In this sample, you'll see how to use [ML.NET](https://www.microsoft.com/net/learn/apps/machine-learning-and-ai/ml-dotnet) to detect anomalies in time series data.