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machinelearning-samples
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Migration/v1.2.0 (#568) 

* Upgrade ML.NET and AutoML versions

* Remove hardcoded versions

* Changed hardcoded version

* Changed hardcoded version

* Changed hardcoded version

* Changed hardcoded versions

* Corrected ML version

* Changed hardcoded version

* Changed hardcoded version

* Changed hardcoded version

* Changed hardcoded versions

* Changed hardcoded version

* Changed hardcoded tensorflow version

* Changed hardcoded tensorflow version

* Changed hardcoded versions

* Updated solution layout after 1.2 migration

* Update ML.NET version in end-to-end readme's

* Updated readme's c# getting started with v1.2

* Updated F# getting started readme's v1.2
This commit is contained in:
Brigit Murtaugh 2019-07-18 17:48:37 -07:00 коммит произвёл Cesar De la Torre
Родитель fa9f2aa73d
Коммит 81ec2808f4
63 изменённых файлов: 118 добавлений и 93 удалений
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samples/Directory.Build.props
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<Project>
<PropertyGroup>
<MicrosoftMLVersion>1.1.0</MicrosoftMLVersion>
<MicrosoftMLPreviewVersion>0.13.0</MicrosoftMLPreviewVersion>
<MicrosoftMLVersion>1.2.0</MicrosoftMLVersion>
<MicrosoftMLPreviewVersion>0.14.0</MicrosoftMLPreviewVersion>
</PropertyGroup>
</Project>

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samples/csharp/end-to-end-apps/AnomalyDetection-Sales/README.md
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| 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)

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samples/csharp/end-to-end-apps/DeepLearning_ImageClassification_TensorFlow/README.md
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| 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

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samples/csharp/end-to-end-apps/DeepLearning_ImageClassification_TensorFlow/TensorFlowImageClassification/TensorFlowImageClassification.csproj
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<ItemGroup>
<PackageReference Include="Microsoft.AspNetCore.App" />
<PackageReference Include="Microsoft.AspNetCore.Razor.Design" Version="2.2.0" PrivateAssets="All" />
<PackageReference Include="Microsoft.Extensions.ML" Version="0.12.0" />
<PackageReference Include="Microsoft.ML" Version="1.1.0" />
<PackageReference Include="Microsoft.ML.ImageAnalytics" Version="1.1.0" />
<PackageReference Include="Microsoft.ML.TensorFlow" Version="0.13.0" />
<PackageReference Include="Microsoft.Extensions.ML" Version="$(MicrosoftMLPreviewVersion)" />
<PackageReference Include="Microsoft.ML" Version="$(MicrosoftMLVersion)" />
<PackageReference Include="Microsoft.ML.ImageAnalytics" Version="$(MicrosoftMLVersion)" />
<PackageReference Include="Microsoft.ML.TensorFlow" Version="$(MicrosoftMLVersion)" />
<PackageReference Include="Microsoft.VisualStudio.Web.CodeGeneration.Design" Version="2.2.3" />
</ItemGroup>

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samples/csharp/end-to-end-apps/DeepLearning_ObjectDetection_Onnx/README.md
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| 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.

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samples/csharp/end-to-end-apps/MulticlassClassification-GitHubLabeler/README.md
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| 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.

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samples/csharp/end-to-end-apps/Recommendation-MovieRecommender/MovieRecommender/movierecommender/MovieRecommender.csproj
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<PackageReference Include="Microsoft.AspNetCore.App" />
<PackageReference Include="Microsoft.ML" Version="$(MicrosoftMLVersion)" />
<PackageReference Include="Microsoft.AspNetCore.Razor.Design" Version="2.2.0" PrivateAssets="All" />
<PackageReference Include="Microsoft.Extensions.ML" Version="0.12.0" />
<PackageReference Include="Microsoft.Extensions.ML" Version="$(MicrosoftMLPreviewVersion)" />
</ItemGroup>
<ItemGroup>
<Content Update="wwwroot\images\smileybob.png">

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samples/csharp/end-to-end-apps/Recommendation-MovieRecommender/README.md
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| 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)

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samples/csharp/end-to-end-apps/Regression-SalesForecast/README.md
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| 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).

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samples/csharp/end-to-end-apps/Regression-SalesForecast/src/eShopDashboard/eShopDashboard.csproj
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<PackageReference Include="Serilog.Sinks.Seq" Version="4.0.0" />
<PackageReference Include="Swashbuckle.AspNetCore" Version="4.0.1" />
<PackageReference Include="TinyCsvParser" Version="2.0.0" />
<PackageReference Include="Microsoft.Extensions.ML" Version="0.12.0" />
<PackageReference Include="Microsoft.Extensions.ML" Version="$(MicrosoftMLPreviewVersion)" />
</ItemGroup>
<ItemGroup>

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samples/csharp/end-to-end-apps/ScalableMLModelOnWebAPI-IntegrationPkg/README.md
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| 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.

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samples/csharp/end-to-end-apps/ScalableMLModelOnWebAPI-IntegrationPkg/src/Scalable.WebAPI/Scalable.WebAPI.csproj
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</PropertyGroup>
<ItemGroup>
<PackageReference Include="Microsoft.Extensions.ML" Version="0.12.0" />
<PackageReference Include="Microsoft.Extensions.ML" Version="$(MicrosoftMLPreviewVersion)" />
<PackageReference Include="Microsoft.ML.FastTree" Version="$(MicrosoftMLVersion)" />
<PackageReference Include="Microsoft.AspNetCore.App" />
</ItemGroup>

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samples/csharp/end-to-end-apps/ScalableMLModelOnWebAPI/README.md
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@ -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.

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samples/csharp/end-to-end-apps/ScalableSentimentAnalysisBlazorWebApp/BlazorSentimentAnalysis.Server/BlazorSentimentAnalysis.Server.csproj
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<ItemGroup>
<PackageReference Include="Microsoft.AspNetCore.Blazor.Server" Version="3.0.0-preview6.19307.2" />
<PackageReference Include="Microsoft.AspNetCore.Mvc.NewtonsoftJson" Version="3.0.0-preview6.19307.2" />
<PackageReference Include="Microsoft.Extensions.ML" Version="0.12.0" />
<PackageReference Include="Microsoft.ML.FastTree" Version="1.1.0" />
<PackageReference Include="Microsoft.Extensions.ML" Version="$(MicrosoftMLPreviewVersion)" />
<PackageReference Include="Microsoft.ML.FastTree" Version="$(MicrosoftMLVersion)" />
</ItemGroup>
<ItemGroup>

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samples/csharp/end-to-end-apps/ScalableSentimentAnalysisBlazorWebApp/README.md
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| 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

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samples/csharp/getting-started/AnomalyDetection_PowerMeterReadings/README.md
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| 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.

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samples/csharp/getting-started/AnomalyDetection_Sales/README.md
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| 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.

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samples/csharp/getting-started/BinaryClassification_CreditCardFraudDetection/Readme.md
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| 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.

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samples/csharp/getting-started/BinaryClassification_HeartDiseaseDetection/README.md
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| 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**.

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samples/csharp/getting-started/BinaryClassification_SentimentAnalysis/README.md
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| 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**.

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samples/csharp/getting-started/BinaryClassification_SpamDetection/README.md
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| 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**.

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samples/csharp/getting-started/Clustering_CustomerSegmentation/README.md
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| 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

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samples/csharp/getting-started/Clustering_Iris/READMe.md
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| 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**.

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samples/csharp/getting-started/DatabaseIntegration/DatabaseIntegration/DatabaseIntegration.csproj
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<ItemGroup>
<PackageReference Include="Microsoft.EntityFrameworkCore.Design" Version="3.0.0-preview.19074.3" />
<PackageReference Include="Microsoft.EntityFrameworkCore.Sqlite" Version="3.0.0-preview.19074.3" />
<PackageReference Include="Microsoft.ML" Version="1.0.0-preview" />
<PackageReference Include="Microsoft.ML.LightGBM" Version="1.0.0-preview" />
<PackageReference Include="Microsoft.ML.FastTree" Version="1.0.0-preview" />
<PackageReference Include="Microsoft.ML" Version="$(MicrosoftMLVersion)" />
<PackageReference Include="Microsoft.ML.LightGBM" Version="$(MicrosoftMLVersion)" />
<PackageReference Include="Microsoft.ML.FastTree" Version="$(MicrosoftMLVersion)" />
</ItemGroup>
<ItemGroup>
<Folder Include="Common\" />

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samples/csharp/getting-started/DeepLearning_ImageClassification_TensorFlow/README.md
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| 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

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samples/csharp/getting-started/DeepLearning_ObjectDetection_Onnx/ObjectDetectionConsoleApp/ObjectDetection.csproj
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<ItemGroup>
<PackageReference Include="Microsoft.ML" Version="$(MicrosoftMLVersion)" />
<PackageReference Include="Microsoft.ML.ImageAnalytics" Version="$(MicrosoftMLVersion)" />
<PackageReference Include="Microsoft.ML.OnnxTransformer" Version="$(MicrosoftMLPreviewVersion)" />
<PackageReference Include="Microsoft.ML.OnnxTransformer" Version="$(MicrosoftMLVersion)" />
</ItemGroup>
<ItemGroup>

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samples/csharp/getting-started/DeepLearning_ObjectDetection_Onnx/README.md
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| 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.

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samples/csharp/getting-started/DeepLearning_TensorFlowEstimator/README.md
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| 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.

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samples/csharp/getting-started/LargeDatasets/README.md
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| 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.

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samples/csharp/getting-started/MatrixFactorization_MovieRecommendation/README.md
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| 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.

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samples/csharp/getting-started/MatrixFactorization_ProductRecommendation/Readme.md
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| 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.

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samples/csharp/getting-started/MulticlassClassification_Iris/README.md
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| 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**.

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samples/csharp/getting-started/MulticlassClassification_MNIST/README.md
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| 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.

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samples/csharp/getting-started/Ranking_Web/README.md
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| 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.

4
samples/csharp/getting-started/Ranking_Web/WebRanking/WebRanking.csproj
Просмотреть файл

@ -6,8 +6,8 @@
</PropertyGroup>
<ItemGroup>
<PackageReference Include="Microsoft.ML" Version="1.1.0" />
<PackageReference Include="Microsoft.ML.LightGbm" Version="1.1.0" />
<PackageReference Include="Microsoft.ML" Version="$(MicrosoftMLVersion)" />
<PackageReference Include="Microsoft.ML.LightGbm" Version="$(MicrosoftMLVersion)" />
</ItemGroup>
</Project>

2
samples/csharp/getting-started/Regression_BikeSharingDemand/README.md
Просмотреть файл

@ -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.

2
samples/csharp/getting-started/Regression_TaxiFarePrediction/README.md
Просмотреть файл

@ -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**.

47
samples/csharp/v1.1.0-Samples.sln Normal file
Просмотреть файл

@ -0,0 +1,47 @@

Microsoft Visual Studio Solution File, Format Version 12.00
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VisualStudioVersion = 16.0.29009.5
MinimumVisualStudioVersion = 10.0.40219.1
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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}"
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{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
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GlobalSection(NestedProjects) = preSolution
{BACF7028-6A68-421B-A7CA-F5A10D822ED4} = {6D589303-EC5C-405C-B9F6-06FE3500FCCB}
{8B051595-03CD-4026-91BB-403F90CF8526} = {6D589303-EC5C-405C-B9F6-06FE3500FCCB}
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GlobalSection(ExtensibilityGlobals) = postSolution
SolutionGuid = {98369941-33DD-450C-A410-B9A91C8CDE91}
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EndGlobal

26
samples/csharp/v1.0.0-All-Samples.sln → samples/csharp/v1.2.0-All-Samples.sln
Просмотреть файл

@ -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}

2
samples/fsharp/end-to-end-apps/MulticlassClassification-GitHubLabeler/README.md
Просмотреть файл

@ -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.

2
samples/fsharp/getting-started/BinaryClassification_CreditCardFraudDetection/Readme.md
Просмотреть файл

@ -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.

2
samples/fsharp/getting-started/BinaryClassification_HeartDiseaseDetection/README.md
Просмотреть файл

@ -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**.

2
samples/fsharp/getting-started/BinaryClassification_SentimentAnalysis/README.md
Просмотреть файл

@ -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 |
------------------------------------

2
samples/fsharp/getting-started/BinaryClassification_SpamDetection/README.md
Просмотреть файл

@ -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**.

2
samples/fsharp/getting-started/Clustering_CustomerSegmentation/README.md
Просмотреть файл

@ -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

2
samples/fsharp/getting-started/Clustering_Iris/README.md
Просмотреть файл

@ -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**.

2
samples/fsharp/getting-started/DeepLearning_ImageClassification_TensorFlow/ImageClassification/ImageClassification.Score.fsproj
Просмотреть файл

@ -12,7 +12,7 @@
<ItemGroup>
<PackageReference Include="Microsoft.ML" Version="$(MicrosoftMLVersion)" />
<PackageReference Include="Microsoft.ML.ImageAnalytics" Version="$(MicrosoftMLVersion)" />
<PackageReference Include="Microsoft.ML.TensorFlow" Version="0.12.0" />
<PackageReference Include="Microsoft.ML.TensorFlow" Version="$(MicrosoftMLVersion)" />
</ItemGroup>
</Project>

2
samples/fsharp/getting-started/DeepLearning_ImageClassification_TensorFlow/README.md
Просмотреть файл

@ -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

2
samples/fsharp/getting-started/DeepLearning_ObjectDetection_Onnx/ObjectDetectionConsoleApp/ObjectDetection.fsproj
Просмотреть файл

@ -12,7 +12,7 @@
<ItemGroup>
<PackageReference Include="Microsoft.ML" Version="$(MicrosoftMLVersion)" />
<PackageReference Include="Microsoft.ML.ImageAnalytics" Version="$(MicrosoftMLVersion)" />
<PackageReference Include="Microsoft.ML.OnnxTransformer" Version="0.12.0-preview" />
<PackageReference Include="Microsoft.ML.OnnxTransformer" Version="$(MicrosoftMLVersion)" />
</ItemGroup>
</Project>

2
samples/fsharp/getting-started/DeepLearning_ObjectDetection_Onnx/README.md
Просмотреть файл

@ -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.

2
samples/fsharp/getting-started/DeepLearning_TensorFlowEstimator/ImageClassification.Predict/ImageClassification.Predict.fsproj
Просмотреть файл

@ -12,7 +12,7 @@
<ItemGroup>
<PackageReference Include="Microsoft.ML" Version="$(MicrosoftMLVersion)" />
<PackageReference Include="Microsoft.ML.ImageAnalytics" Version="$(MicrosoftMLVersion)" />
<PackageReference Include="Microsoft.ML.TensorFlow" Version="0.12.0" />
<PackageReference Include="Microsoft.ML.TensorFlow" Version="$(MicrosoftMLVersion)" />
</ItemGroup>
</Project>

2
samples/fsharp/getting-started/DeepLearning_TensorFlowEstimator/ImageClassification.Train/ImageClassification.Train.fsproj
Просмотреть файл

@ -12,7 +12,7 @@
<ItemGroup>
<PackageReference Include="Microsoft.ML" Version="$(MicrosoftMLVersion)" />
<PackageReference Include="Microsoft.ML.ImageAnalytics" Version="$(MicrosoftMLVersion)" />
<PackageReference Include="Microsoft.ML.TensorFlow" Version="0.12.0" />
<PackageReference Include="Microsoft.ML.TensorFlow" Version="$(MicrosoftMLVersion)" />
</ItemGroup>
</Project>

2
samples/fsharp/getting-started/DeepLearning_TensorFlowEstimator/README.md
Просмотреть файл

@ -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

2
samples/fsharp/getting-started/MatrixFactorization_ProductRecommendation/ProductRecommender/ProductRecommender.fsproj
Просмотреть файл

@ -8,7 +8,7 @@
<ItemGroup>
<PackageReference Include="Microsoft.ML" Version="$(MicrosoftMLVersion)" />
<PackageReference Include="Microsoft.ML.Recommender" Version="0.12.0" />
<PackageReference Include="Microsoft.ML.Recommender" Version="$(MicrosoftMLPreviewVersion)" />
</ItemGroup>
<ItemGroup>

2
samples/fsharp/getting-started/MatrixFactorization_ProductRecommendation/Readme.md
Просмотреть файл

@ -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.

2
samples/fsharp/getting-started/MulticlassClassification_Iris/README.md
Просмотреть файл

@ -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**.

2
samples/fsharp/getting-started/MulticlassClassification_mnist/README.md
Просмотреть файл

@ -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.

2
samples/fsharp/getting-started/Regression_BikeSharingDemand/README.md
Просмотреть файл

@ -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.

2
samples/fsharp/getting-started/Regression_TaxiFarePrediction/README.md
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@ -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**.

2
samples/fsharp/getting-started/SpikeDetection_ShampooSales/README.md
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| 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.

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samples/fsharp/getting-started/SpikeDetection_ShampooSales/ShampooSales/ShampooSalesConsoleApp/ShampooSales.fsproj
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<ItemGroup>
<PackageReference Include="Microsoft.ML" Version="$(MicrosoftMLVersion)" />
<PackageReference Include="Microsoft.ML.TimeSeries" Version="0.12.0" />
<PackageReference Include="Microsoft.ML.TimeSeries" Version="$(MicrosoftMLVersion)" />
</ItemGroup>
</Project>

2
samples/fsharp/getting-started/TimeSeries_PowerAnomalyDetection/PowerAnomalyDetection/PowerAnomalyDetection.fsproj
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<ItemGroup>
<PackageReference Include="Microsoft.ML" Version="$(MicrosoftMLVersion)" />
<PackageReference Include="Microsoft.ML.TimeSeries" Version="0.12.0" />
<PackageReference Include="Microsoft.ML.TimeSeries" Version="$(MicrosoftMLVersion)" />
</ItemGroup>
</Project>

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samples/fsharp/getting-started/TimeSeries_PowerAnomalyDetection/README.md
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| 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.