C# 人工智能

参考：https://dotnet.microsoft.com/apps/machinelearning-ai/ml-dotnet

ML.NET

An open source and cross-platform machine learning framework

Get started Model Builder

Supported on Windows, Linux, and macOS

 

Built for .NET developers

With ML.NET, you can use your existing .NET skills to easily integrate ML into your .NET apps without any prior ML experience.

 

Custom ML made easy with AutoML

ML.NET offers AutoML and productive tools to help you easily build, train, and deploy high-quality custom ML models.

 

Extended with TensorFlow & more

ML.NET allows you to leverage other popular ML libraries like Infer.NET, TensorFlow, and ONNX for additional ML scenarios.

 

Trusted and proven at scale

Use the same ML framework used by recognized Microsoft products like Power BI, Microsoft Defender, Outlook, and Bing.

• C#
   
• F#
   

//Step 1. Create an ML Context
var ctx = new MLContext();

//Step 2. Read in the input data from a text file for model training
IDataView trainingData = ctx.Data
    .LoadFromTextFile<ModelInput>(dataPath, hasHeader: true);

//Step 3. Build your data processing and training pipeline
var pipeline = ctx.Transforms.Text
    .FeaturizeText("Features", nameof(SentimentIssue.Text))
    .Append(ctx.BinaryClassification.Trainers
        .LbfgsLogisticRegression("Label", "Features"));

//Step 4. Train your model
ITransformer trainedModel = pipeline.Fit(trainingData);

//Step 5. Make predictions using your trained model
var predictionEngine = ctx.Model
    .CreatePredictionEngine<ModelInput, ModelOutput>(trainedModel);

var sampleStatement = new ModelInput() { Text = "This is a horrible movie" };

var prediction = predictionEngine.Predict(sampleStatement);

Built for .NET developers

With ML.NET, you can create custom ML models using C# or F# without having to leave the .NET ecosystem.

ML.NET lets you re-use all the knowledge, skills, code, and libraries you already have as a .NET developer so that you can easily integrate machine learning into your web, mobile, desktop, games, and IoT apps.

Dig deeper: What is ML.NET?

 

Sentiment analysis

Analyze the sentiment of customer reviews using a binary classification algorithm.

 

Product recommendation

Recommend products based on purchase history using a matrix factorization algorithm.

 

Price prediction

Predict taxi fares based on parameters such as distance traveled using a regression algorithm.

 

Customer segmentation

Identify groups of customers with similar profiles using a clustering algorithm.

 

Object detection

Recognize objects in an image using an ONNX deep learning model.

 

Fraud detection

Detect fraudulent credit card transactions using a binary classification algorithm.

 

Sales spike detection

Detect spikes and changes in product sales using an anomaly detection model.

 

Image classification

Classify images (for example, broccoli vs. pizza) using a TensorFlow deep learning model.

 

Sales forecasting

Forecast future sales for products using a regression algorithm.

You can find more ML.NET samples on GitHub, or take a look at the ML.NET tutorials.

Custom ML made easy with AutoML

ML.NET offers Model Builder (a simple UI tool) and ML.NET CLI to make it super easy to build custom ML Models.

These tools use Automated ML (AutoML), a cutting edge technology that automates the process of building best performing models for your Machine Learning scenario. All you have to do is load your data, and AutoML takes care of the rest of the model building process.

Explore ML.NET Model Builder

• Model Builder
   
• Command Line
   

• 
 
• 
 
• 
 
• 
 
• 
 
• 

View sample code on GitHub

Extended with TensorFlow & more

ML.NET has been designed as an extensible platform so that you can consume other popular ML frameworks (TensorFlow, ONNX, Infer.NET, and more) and have access to even more machine learning scenarios, like image classification, object detection, and more.

Data sourced from Machine Learning at Microsoft with ML.NET paper. Results for sentiment analysis, using ~900 MB of an Amazon review dataset. Higher accuracy and lower runtime are better.

High performance and accuracy

Using a 9GB Amazon review data set, ML.NET trained a sentiment analysis model with 95% accuracy. Other popular machine learning frameworks failed to process the dataset due to memory errors. Training on 10% of the data set, to let all the frameworks complete training, ML.NET demonstrated the highest speed and accuracy.

The performance evaluation found similar results in other machine learning scenarios, including click-through rate prediction and flight delay prediction.

Read the ML.NET performance paper