How to use machine learning and AI on dot net web applications?

Generally, you can use AI services in .NET like Azure cognitive services. It is just to use AI APIs. We need to identify use cases like Text recognizer, speech, search, OCR, vision, language etc. 

If you want to build your own AI models then you can ML.NET or ML designer to do predictions, classifications, regressions and so on.

Regards

Rijwan

PyTorch.NET library can be used for this purpose.

You can install Pytorch.NET library from Visual studio Nuget package manager in your .NET Project.

dotnet add package PyTorch.NET
 

Once it is installed and added to your project you can import the package in your source code using :

using Torch;
using TorchSharp;
 

This will allow you to use PyTorch classes and methods in your .NET applciations.

Keep checking PyTorch documentation for any new classes, features updates that can be imported in your .NET application.

https://pytorch.org/tutorials/beginner/blitz/neural_networks_tutorial.html

You can refer below ML.NET Tutorial, this may help you and also can find below sample code. 

https://dotnet.microsoft.com/en-us/learn/ml-dotnet/get-started-tutorial/create

Integrating machine learning and AI into .NET web applications can bring powerful capabilities and enhance user experiences. While Python is a popular language for machine learning and AI, it is possible to leverage its capabilities within a .NET environment. Here are a few approaches you can consider:

1. Using Python with .NET: You can use Python libraries for machine learning and AI within your .NET web application by leveraging the Python.NET library. This library allows you to call Python code from your .NET application and exchange data seamlessly. You can install the Python.NET package using pip and then use it to import and execute Python code within your .NET application.

2. Using ML.NET: ML.NET is a cross-platform, open-source machine learning framework developed by Microsoft. It allows you to build custom machine learning models using C# or F# without requiring extensive knowledge of Python. ML.NET provides a wide range of pre-built machine learning algorithms and supports tasks such as classification, regression, clustering, and recommendation systems.

3. Using TensorFlow.NET: TensorFlow is a popular machine learning framework developed by Google. TensorFlow.NET is a .NET binding for TensorFlow that allows you to use TensorFlow models within your .NET web application. You can train your models in Python using TensorFlow and then load and use them in your .NET application using TensorFlow.NET.

Here's a simple example of using ML.NET to classify text sentiment in a .NET web application:

using Microsoft.ML;
using Microsoft.ML.Data;

public class SentimentData
{
    [LoadColumn(0)]
    public string Text { get; set; }

    [LoadColumn(1)]
    [ColumnName("Label")]
    public bool Sentiment { get; set; }
}

public class SentimentPrediction
{
    [ColumnName("PredictedLabel")]
    public bool Prediction { get; set; }
}

public class SentimentAnalysis
{
    public bool AnalyzeSentiment(string text)
    {
        var context = new MLContext();
        var data = context.Data.LoadFromTextFile("sentiment-data.csv", separatorChar: ',');
        var pipeline = context.Transforms.Text.FeaturizeText("Features", nameof(SentimentData.Text))
            .Append(context.Transforms.Conversion.MapValueToKey("Label"))
            .Append(context.Transforms.NormalizeMinMax("Features"))
            .Append(context.Transforms.Conversion.MapKeyToValue("PredictedLabel"))
            .Append(context.Transforms.Conversion.MapKeyToValue("Label"));

        var model = pipeline.Fit(data);
        var predictionEngine = context.Model.CreatePredictionEngine(model);

        var sentimentData = new SentimentData { Text = text };
        var prediction = predictionEngine.Predict(sentimentData);

        return prediction.Prediction;
    }
}

In this example, we use ML.NET to train a sentiment analysis model on a dataset stored in a CSV file. The AnalyzeSentiment method takes a text input and predicts its sentiment using the trained model.

Remember to install the necessary NuGet packages for ML.NET and TensorFlow.NET to use these examples.