AI Model Training with .NET
Welcome to the hub for learning and implementing AI model training within the .NET platform. Discover how to leverage powerful libraries, frameworks, and tools to build, train, and iterate on your machine learning models.
Getting Started with .NET ML Training
Dive into the fundamental concepts and essential tools for training AI models with .NET. Whether you're a beginner or looking to deepen your expertise, these resources will guide you.
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ML.NET Fundamentals
Learn the basics of ML.NET, Microsoft's open-source, cross-platform machine learning framework for .NET developers.
Explore ML.NET Documentation -
Introduction to AI Concepts
Understand core AI and Machine Learning principles, including supervised and unsupervised learning, deep learning, and model evaluation.
Read AI Basics Guide
Key Libraries and Frameworks
Explore the rich ecosystem of libraries and frameworks that empower .NET developers in AI model training.
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ML.NET API Reference
Detailed documentation for the ML.NET API, covering data loading, transformations, trainers, and model evaluation.
View ML.NET API -
Integration with ONNX Runtime
Learn how to train models and integrate them using ONNX Runtime for cross-platform inference and training capabilities.
ONNX Runtime for .NET -
TensorFlow.NET and SciSharp STACK
Discover the power of .NET bindings for TensorFlow and other scientific computing libraries for advanced ML tasks.
Explore SciSharp STACK
Practical Training Examples
See real-world examples and tutorials demonstrating how to train various types of AI models with .NET.
Sentiment Analysis Model Training
Train a model to classify text sentiment using ML.NET.
using Microsoft.ML;
using Microsoft.ML.Data;
var mlContext = new MLContext();
// Define data schema
public class SentimentData
{
[LoadColumn(0)]
public string SentimentText;
[LoadColumn(1), ColumnName("Label")]
public bool Sentiment;
}
// Load dataset
var trainingDataView = mlContext.Data.LoadFromTextFile("sentiment.tsv", separatorChar: '\t', hasHeader: false);
// Data processing pipeline
var pipeline = mlContext.Transforms.Text.FeaturizeText(outputColumnName: "Features", inputColumnName: nameof(SentimentData.SentimentText))
.Append(mlContext.BinaryClassification.Trainers.SdcaLogisticRegression(labelColumnName: "Label", featureColumnName: "Features"));
// Train the model
var model = pipeline.Fit(trainingDataView);
// Save the model
mlContext.Model.Save(model, trainingDataView.Schema, "sentiment_model.zip");
Image Classification with .NET
Walk through training an image classification model using pre-trained models or custom datasets.
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Tutorial: Image Classification
Step-by-step guide to building an image classifier in .NET.
View Image Classification Tutorial
Community and Support
Connect with other .NET developers, share your experiences, and get help with your AI training projects.
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MSDN Community Forums
Ask questions, share solutions, and engage in discussions about .NET AI development.
Join the .NET AI Forum -
GitHub Repositories
Explore community-driven projects, contribute to open-source ML.NET initiatives, and find useful code samples.
Discover .NET AI on GitHub