diff --git a/samples/Directory.Build.props b/samples/Directory.Build.props index e9ef49c7..c9388470 100644 --- a/samples/Directory.Build.props +++ b/samples/Directory.Build.props @@ -1,8 +1,8 @@ - 1.1.0 - 0.13.0 + 1.2.0 + 0.14.0 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 4bc8bf69..9d457a51 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 | |----------------|-------------------|-------------------------------|-------------|-----------|---------------------|---------------------------|-----------------------------| -| v1.1.0 | 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 | +| 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 | ![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 0358eb94..8f0810ae 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 | |----------------|-------------------|-------------------------------|-------------|-----------|---------------------|---------------------------|-----------------------------| -| v1.1.0 | Dynamic API | up-to-date | Console app | Images and text labels | Images classification | TensorFlow model | DeepLearning model | +| v1.2.0 | 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 c15cdc5a..1120d25b 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 642618c7..191a7010 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 | |----------------|-------------------|-------------------------------|-------------|-----------|---------------------|---------------------------|-----------------------------| -| v1.1.0 | Dynamic API | Up-to-date | End-End app | image files | Object Detection | Deep Learning | Tiny Yolo2 ONNX model | +| v1.2.0 | 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 caeca35f..1fb8f74c 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 | |----------------|-------------------|-------------------------------|-------------|-----------|---------------------|---------------------------|-----------------------------| -| v1.1.0 | Dynamic API | Up-to-date | Console app | .csv file and GitHub issues | Issues classification | Multi-class classification | SDCA multi-class classifier, AveragedPerceptronTrainer | +| 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 | 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 34bb09e0..7a76fffe 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 a26cfacd..58bc759b 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 | |----------------|-------------------|-------------------------------|-------------|-----------|---------------------|---------------------------|-----------------------------| -|v1.1.0 | Dynamic API | up-to-date | End-End app | .csv | Movie Recommendation | Recommendation | Field Aware Factorization Machines | +|v1.2.0 | 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 21c2e4a8..5f0140f2 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 | |----------------|-------------------|-------------------------------|-------------|-----------|---------------------|---------------------------|-----------------------------| -| v1.1.0 | Dynamic API | Up-to-date | ASP.NET Core web app and Console app | SQL Server and .csv files | Sales forecast | Regression | FastTreeTweedie Regression | +| 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 | 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 6033c8f7..8b8adeed 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 5a3a4f61..0e99f338 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 | |----------------|-------------------------------|-------------|-----------|---------------------|---------------------------|-----------------------------| -| v1.1.0 | Up-to-date | ASP.NET Core 2.2 WebAPI | Single data sample | Sentiment Analysis | Binary classification | Linear Classification | +| v1.2.0 | 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 056d20b7..6135ddc2 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 fbdd63b5..8a222844 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 | |----------------|-------------------------------|-------------|-----------|---------------------|---------------------------|-----------------------------| -| v1.0.0 | Up-to-date | ASP.NET Core 2.2 WebAPI | Single data sample | Sentiment Analysis | Binary classification | Linear Classification | +| v1.2.0 | 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 ba53b2a5..ed00e7e4 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 5ad05877..df86c085 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 | |----------------|-------------------------------|-------------|-----------|---------------------|---------------------------|-----------------------------| -| v1.1.0 | Up-to-date | Blazor / ASP.NET Core 3.0 Preview 6 | Single data sample | Sentiment Analysis | Binary classification | Linear Classification | +| v1.2.0 | 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 aef3b2c1..155e518f 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 | |----------------|-------------------|-------------------------------|-------------|-----------|---------------------|---------------------------|-----------------------------| -| v1.1.0 | Dynamic API | Up-to-date | Console app | .csv files | Power Meter Anomaly Detection | Time Series- Anomaly Detection | SsaSpikeDetection | +| v1.2.0 | 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 d907a42f..692af357 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 | |----------------|-------------------|-------------------------------|-------------|-----------|---------------------|---------------------------|-----------------------------| -| v1.1.0 | 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 | +| 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 | 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 0e37d788..aee51226 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 | |----------------|-------------------|-------------------------------|-------------|-----------|---------------------|---------------------------|-----------------------------| -| v1.1.0 | Dynamic API | Up-to-date | Two console apps | .csv file | Fraud Detection | Two-class classification | FastTree Binary Classification | +| v1.2.0 | 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 3c92f028..d7764aa4 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 | |----------------|-------------------|-------------------------------|-------------|-----------|---------------------|---------------------------|-----------------------------| -| v1.1.0 | Dynamic API | Up-to-date | Console app | .txt files | Heart disease classification | Binary classification | FastTree | +| v1.2.0 | 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 0850275b..2d8204ff 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 | |----------------|-------------------|-------------------------------|-------------|-----------|---------------------|---------------------------|-----------------------------| -| v1.1.0 | Dynamic API | up-to-date | Console app | .tsv files | Sentiment Analysis | Two-class classification | Linear Classification | +| v1.2.0 | 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 98bd0cdc..4d2e2a0f 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 | |----------------|-------------------|-------------------------------|-------------|-----------|---------------------|---------------------------|-----------------------------| -| v1.1.0 | Dynamic API | Might need to update project structure to match template | Console app | .tsv files | Spam detection | Two-class classification | Averaged Perceptron (linear learner) | +| 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) | 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 de090627..d6ca4e59 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 | |----------------|-------------------|-------------------------------|-------------|-----------|---------------------|---------------------------|-----------------------------| -| v1.1.0 | Dynamic API | Up-to-date | Console app | .csv files | Customer segmentation | Clustering | K-means++ | +| v1.2.0 | 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 4e333f45..86a27a41 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 | |----------------|-------------------|-------------------------------|-------------|-----------|---------------------|---------------------------|-----------------------------| -| v1.1.0 | Dynamic API | Up-to-date | Console app | .txt file | Clustering Iris flowers | Clustering | K-means++ | +| v1.2.0 | 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 9e240ebc..b1ca9e36 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 a675fbfc..90e4fbda 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 | |----------------|-------------------|-------------------------------|-------------|-----------|---------------------|---------------------------|-----------------------------| -| v1.1.0 | Dynamic API | up-to-date | Console app | Images and text labels | Images classification | TensorFlow Inception5h | DeepLearning model | +| v1.2.0 | 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 055d99dc..6911551c 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 97883971..893ee583 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 | |----------------|-------------------|-------------------------------|-------------|-----------|---------------------|---------------------------|-----------------------------| -| v1.1.0 | Dynamic API | Up-to-date | Console app | image files | Object Detection | Deep Learning | Tiny Yolo2 ONNX model | +| v1.2.0 | 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 a6cabbf5..ed5d14b7 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 | |----------------|-------------------|-------------------------------|-------------|-----------|---------------------|---------------------------|-----------------------------| -| v1.1.0 | Dynamic API | Up-to-date | Console app | .tsv + image files | Image classification | featurization + classification | deep neural network + SDCA | +| v1.2.0 | 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 4437aac5..a9f04fbd 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 | |----------------|-------------------|-------------------------------|-------------|-----------|---------------------|---------------------------|-----------------------------| -| v1.1.0 | Dynamic API | Up-to-date | Console app | .txt files | Large datasets | Binary classification | FieldAwareFactorizationMachine | +| v1.2.0 | 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 dbc3da68..d8f52bb9 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 | |----------------|-------------------|-------------------------------|-------------|-----------|---------------------|---------------------------|-----------------------------| -| v1.1.0 | Dynamic API | Up-to-date | Console app | .csv files | Recommendation | Matrix Factorization | MatrixFactorizationTrainer| +| v1.2.0 | 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 e70b3b97..b1aceb00 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 | |----------------|-------------------|-------------------------------|-------------|-----------|---------------------|---------------------------|-----------------------------| -|v1.1.0 | Dynamic API | Up-to-date | Console app | .txt files | Recommendation | Matrix Factorization | MatrixFactorizationTrainer (One Class)| +|v1.2.0 | 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 60e05484..a2c51f8c 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 | |----------------|-------------------|-------------------------------|-------------|-----------|---------------------|---------------------------|-----------------------------| -| v1.1.0 | Dynamic API | Up-to-date | Console app | .txt files | Iris flowers classification | Multi-class classification | Sdca Multi-class | +| v1.2.0 | 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 c94bb66d..20e6d7c4 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 | |----------------|-------------------|-------------------------------|-------------|-----------|---------------------|---------------------------|-----------------------------| -| v1.1.0 | Dynamic API | Up-to-date | Console app | .csv files | MNIST classification | Multi-class classification | Sdca Multi-class | +| v1.2.0 | 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 3d0656d4..c2d1dcd0 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 | |----------------|-------------------|-------------------------------|-------------|-----------|---------------------|---------------------------|-----------------------------| -| v1.1.0 | Dynamic API | Up-to-date | Console app | .csv file | Ranking search engine results | Ranking | LightGBM | +| v1.2.0 | 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 fbca6af6..b01a0bb3 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 7f20a165..cb59b277 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 | |----------------|-------------------|-------------------------------|-------------|-----------|---------------------|---------------------------|-----------------------------| -| v1.1.0 | Dynamic API | Up-to-date | Console app | .csv files | Demand prediction | Regression | Fast Tree regressor compared to additional regression algorithms| +| v1.2.0 | 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 1962efdd..7d0bc85a 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 | |----------------|-------------------|-------------------------------|-------------|-----------|---------------------|---------------------------|-----------------------------| -| v1.1.0 | Dynamic API | Up-to-date | Console app | .csv files | Price prediction | Regression | Sdca Regression | +| v1.2.0 | 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/v1.1.0-Samples.sln b/samples/csharp/v1.1.0-Samples.sln new file mode 100644 index 00000000..ec343e8f --- /dev/null +++ b/samples/csharp/v1.1.0-Samples.sln @@ -0,0 +1,47 @@ + +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/csharp/v1.0.0-All-Samples.sln b/samples/csharp/v1.2.0-All-Samples.sln similarity index 95% rename from samples/csharp/v1.0.0-All-Samples.sln rename to samples/csharp/v1.2.0-All-Samples.sln index 44487c52..7d7f0d06 100644 --- a/samples/csharp/v1.0.0-All-Samples.sln +++ b/samples/csharp/v1.2.0-All-Samples.sln @@ -1,7 +1,7 @@  Microsoft Visual Studio Solution File, Format Version 12.00 -# Visual Studio 15 -VisualStudioVersion = 15.0.28307.705 +# Visual Studio Version 16 +VisualStudioVersion = 16.0.29009.5 MinimumVisualStudioVersion = 10.0.40219.1 Project("{2150E333-8FDC-42A3-9474-1A3956D46DE8}") = "BikeSharingDemand.Solution", "BikeSharingDemand.Solution", "{820E8AF2-A47D-4AB8-A4AF-5CDFF97EBCDF}" EndProject @@ -109,10 +109,6 @@ 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}" @@ -373,22 +369,6 @@ 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 @@ -471,8 +451,6 @@ 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/fsharp/end-to-end-apps/MulticlassClassification-GitHubLabeler/README.md b/samples/fsharp/end-to-end-apps/MulticlassClassification-GitHubLabeler/README.md index a0de87ef..0ce561b3 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 | |----------------|-------------------|-------------------------------|-------------|-----------|---------------------|---------------------------|-----------------------------| -| v1.0.0-preview | Dynamic API | Up-to-date | Console app | .csv file and GitHub issues | Issues classification | Multi-class classification | SDCA multi-class classifier | +| v1.2.0 | 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 ff67ab99..4d9fc0ff 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 | |----------------|-------------------|-------------------------------|-------------|-----------|---------------------|---------------------------|-----------------------------| -| v1.0.0-preview | Dynamic API | Up-to-date | Two console apps | .csv file | Fraud Detection | Two-class classification | FastTree Binary Classification | +| v1.2.0 | 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/fsharp/getting-started/BinaryClassification_HeartDiseaseDetection/README.md b/samples/fsharp/getting-started/BinaryClassification_HeartDiseaseDetection/README.md index 248cad3c..fde8c20e 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 | |----------------|-------------------|-------------------------------|-------------|-----------|---------------------|---------------------------|-----------------------------| -| v1.0.0-preview | Dynamic API | Up-to-date | Console app | .txt files | Heart disease classification | Binary classification | FastTree | +| v1.2.0 | 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/fsharp/getting-started/BinaryClassification_SentimentAnalysis/README.md b/samples/fsharp/getting-started/BinaryClassification_SentimentAnalysis/README.md index 70f7f780..5312ccb8 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 | |----------------|-------------------|-------------------------------|-------------|-----------|---------------------|---------------------------|-----------------------------| -| v1.0.0-preview | Dynamic API | README.md updated | Console app | .tsv files | Sentiment Analysis | Two-class classification | Linear Classification | +| v1.2.0 | 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 d028445a..c9008e72 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 | |----------------|-------------------|-------------------------------|-------------|-----------|---------------------|---------------------------|-----------------------------| -| v1.0.0-preview | Dynamic API | Up-to-date | Console app | .tsv files | Spam detection | Two-class classification | SDCA (linear learner) | +| v1.2.0 | 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 72e7a5bf..c8fd8cbc 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 | |----------------|-------------------|-------------------------------|-------------|-----------|---------------------|---------------------------|-----------------------------| -| v1.0.0-preview | Dynamic API | Up-to-date | Console app | .csv files | Customer segmentation | Clustering | K-means++ | +| v1.2.0 | 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 080a9877..de93903f 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 | |----------------|-------------------|-------------------------------|-------------|-----------|---------------------|---------------------------|-----------------------------| -| v1.0.0-preview | Dynamic API | Up-to-date | Console app | .txt file | Clustering Iris flowers | Clustering | K-means++ | +| v1.2.0 | 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/fsharp/getting-started/DeepLearning_ImageClassification_TensorFlow/ImageClassification/ImageClassification.Score.fsproj b/samples/fsharp/getting-started/DeepLearning_ImageClassification_TensorFlow/ImageClassification/ImageClassification.Score.fsproj index 96b41082..68237733 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 d495eeeb..6d4205c1 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 | |----------------|-------------------|-------------------------------|-------------|-----------|---------------------|---------------------------|-----------------------------| -| v1.0.0-preview | Dynamic API | up-to-date | Console app | Images and text labels | Images classification | TensorFlow Inceptionv3 | DeepLearning model | +| v1.2.0 | 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 2bebcd35..25d6774c 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 738a3b95..090660c1 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 | |----------------|-------------------|-------------------------------|-------------|-----------|---------------------|---------------------------|-----------------------------| -| v1.0.0-preview | Dynamic API | Up-to-date | Console app | .tsv + image files | Object Detection | Deep Learning | Tiny Yolo2 ONNX model | +| v1.2.0 | 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 96b41082..68237733 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 96b41082..68237733 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 6479e2b3..c71aa2c5 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 | |----------------|-------------------|-------------------------------|-------------|-----------|---------------------|---------------------------|-----------------------------| -| v0.10 | Dynamic API | Up-to-date | Console app | .tsv + image files | Image classification | featurization + classification | deep neural network + SDCA | +| v1.2.0 | 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 87843327..269bcc74 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 0c9ca253..7a2e6d6d 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 | |----------------|-------------------|-------------------------------|-------------|-----------|---------------------|---------------------------|-----------------------------| -|v1.0.0-preview | Dynamic API | Up-to-date | Console app | .txt files | Recommendation | Matrix Factorization | MatrixFactorizationTrainer (One Class)| +|v1.2.0 | 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/fsharp/getting-started/MulticlassClassification_Iris/README.md b/samples/fsharp/getting-started/MulticlassClassification_Iris/README.md index f946958b..2d90e90a 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 | |----------------|-------------------|-------------------------------|-------------|-----------|---------------------|---------------------------|-----------------------------| -| v1.0.0-preview | Dynamic API | Up-to-date | Console app | .txt files | Iris flowers classification | Multi-class classification | Sdca Multi-class | +| v1.2.0 | 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/fsharp/getting-started/MulticlassClassification_mnist/README.md b/samples/fsharp/getting-started/MulticlassClassification_mnist/README.md index 77b11b7a..50a36a29 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 | |----------------|-------------------|-------------------------------|-------------|-----------|---------------------|---------------------------|-----------------------------| -| v1.0.0-preview | Dynamic API | Up-to-date | Console app | .csv files | MNIST classification | Multi-class classification | Sdca Multi-class | +| v1.2.0 | 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/fsharp/getting-started/Regression_BikeSharingDemand/README.md b/samples/fsharp/getting-started/Regression_BikeSharingDemand/README.md index 80f19620..7f0891a8 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 | |----------------|-------------------|-------------------------------|-------------|-----------|---------------------|---------------------------|-----------------------------| -| v1.0.0-preview | Dynamic API | Up-to-date | Console app | .csv files | Demand prediction | Regression | Fast Tree regressor compared to additional regression algorithms| +| v1.2.0 | 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/fsharp/getting-started/Regression_TaxiFarePrediction/README.md b/samples/fsharp/getting-started/Regression_TaxiFarePrediction/README.md index 154276c0..bf4743fa 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 | |----------------|-------------------|-------------------------------|-------------|-----------|---------------------|---------------------------|-----------------------------| -| v1.0.0-preview | Dynamic API | Up-to-date | Console app | .csv files | Price prediction | Regression | Sdca Regression | +| v1.2.0 | 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/fsharp/getting-started/SpikeDetection_ShampooSales/README.md b/samples/fsharp/getting-started/SpikeDetection_ShampooSales/README.md index 40bb6ac2..d4cd1429 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 | |----------------|-------------------|-------------------------------|-------------|-----------|---------------------|---------------------------|-----------------------------| -| v1.0.0-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 | +| 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 | 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 c88c9fe5..72d6596b 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 c88c9fe5..72d6596b 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 2f925ccd..27d75240 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 | |----------------|-------------------|-------------------------------|-------------|-----------|---------------------|---------------------------|-----------------------------| -| v1.0.0-preview | Dynamic API | Up-to-date | Console app | .csv files | Power Meter Anomaly Detection | Time Series- Anomaly Detection | SsaSpikeDetection | +| v1.2.0 | 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.