What are the main features of dotnet/machinelearning-samples?

The main features of dotnet/machinelearning-samples are: .NET Machine Learning Integrations, Data Transformation Pipelines, Image and Video Analysis, External Model Loading, ONNX Model Runtimes, Supervised Learning, Time Series Forecasting, Real-Time Object Detection.

Machinelearning Samples - build ML.NET models…

This repository is a collection of reference implementations, templates, and sample galleries for building and integrating machine learning models within the .NET ecosystem. It provides a set of practical demonstrations for implementing machine learning workflows using the ML.NET framework.

The project emphasizes the integration of pre-trained models via the Open Neural Network Exchange format, allowing the execution of external machine learning logic within managed applications. It includes specific examples for loading and executing these standardized models to ensure cross-platform compatibility.

The samples cover a range of supervised learning tasks, including text sentiment classification, image and video analysis for object detection, and time-series forecasting. It also provides implementations for network anomaly detection and tools for hyperparameter optimization and data transformation pipelines.

Features

.NET Machine Learning Integrations - Integrates machine learning models and predictive capabilities specifically within the .NET ecosystem.
Data Transformation Pipelines - Implements frameworks for filtering, cleaning, and modifying data before it is used in model generation.
Image and Video Analysis - Implements machine learning workflows to classify image content and detect objects within real-time video streams.
External Model Loading - Provides capabilities for importing and initializing pre-trained models from the standardized ONNX open format.
ONNX Model Runtimes - Provides runtimes that load and execute ONNX models for cross-framework inference.
Supervised Learning - Implements various classification and regression tasks using models trained on labeled data.
Time Series Forecasting - Implements models and architectures designed for predicting future values in temporal data sequences.
Real-Time Object Detection - Provides tools for identifying and locating specific objects within live video streams or webcam footage.
Custom Data Transform Extensions - Provides mechanisms for defining and registering custom data preprocessing logic within machine learning pipelines.
Feature Extraction - Provides tools for converting raw visual data into meaningful numerical representations for analysis.
Image Classification - Implements systems that assign labels or categories to images based on their visual content.
Hyperparameter Optimization - Implements automated methods for searching and selecting the best configuration parameters for a model.
Dataset-Driven Model Generators - Provides capabilities to generate high-quality machine learning models and the source code required to execute them from datasets.
Sentiment Classifiers - Provides implementation examples for building neural network architectures that classify the emotional tone of text data.
Anomaly Detection - Provides reference implementations for identifying unusual patterns in network traffic and system logs.
AI and Machine Learning - Interactive UI samples for sentiment analysis and prediction.

lazyprogrammer/machine_learning_examples

8,823View on GitHub

This project is a comprehensive collection of practical code examples and implementation libraries for machine learning. It provides a wide array of reference materials for building supervised, unsupervised, and reinforcement learning algorithms. The repository serves as a multi-domain resource, featuring specific implementation suites for financial AI, Bayesian statistical modeling, and deep learning architectures. It includes a framework for training intelligent agents using policy gradients and actor-critic models, as well as practical guides for fine-tuning transformers and utilizing larg

d2l-ai/d2l-en

29,001View on GitHub

This project is an educational platform and research toolkit designed to teach deep learning through a combination of mathematical theory, visual diagrams, and executable code. It provides a comprehensive environment for building, training, and evaluating neural networks, grounding complex concepts in interactive computational notebooks that allow for hands-on experimentation. The framework distinguishes itself by interleaving theoretical foundations—including linear algebra, calculus, and probability—with practical implementations across multiple industry-standard libraries. It supports flex

Smile is a comprehensive JVM machine learning library and statistical computing toolkit. It provides a suite of algorithms for classification, regression, and clustering, implemented natively for Java, Scala, and Kotlin. The project also functions as a deep learning framework, a natural language processing library, and an inference engine for large language models. The library distinguishes itself through GPU acceleration via LibTorch bindings and support for the ONNX model interchange format. It includes specialized capabilities for large language model inference, featuring Byte-Pair Encodin

dotnet/machinelearning

9,329View on GitHub

This is a cross-platform framework for building, training, and deploying custom machine learning models within the .NET ecosystem. It provides a predictive modeling engine for classification, regression, and forecasting tasks, alongside an inference runtime to generate predictions across different hardware architectures. The framework includes a gradient boosting library and supports interoperability with external models via a standardized open format. It features tools for prediction explainability, allowing the analysis of feature importance to debug model behavior and identify bias. The p

dotnetmachinelearning-samples