ML.NET component for Image support
See the version list below for details.
Install-Package Microsoft.ML.ImageAnalytics -Version 1.4.0-preview2
dotnet add package Microsoft.ML.ImageAnalytics --version 1.4.0-preview2
<PackageReference Include="Microsoft.ML.ImageAnalytics" Version="1.4.0-preview2" />
paket add Microsoft.ML.ImageAnalytics --version 1.4.0-preview2
NuGet packages (5)
Showing the top 5 NuGet packages that depend on Microsoft.ML.ImageAnalytics:
ML.NET AutoML: Optimizes an ML pipeline for your dataset, by automatically locating the best feature engineering, model, and hyperparameters
ML.NET component for a statically typed API.
Simple machine learning wrapper for Microsoft.ML
A .NET Core library with functionality to take a simple screenshot of a chessboard to a fully-evaluated position.
GitHub repositories (5)
Showing the top 5 popular GitHub repositories that depend on Microsoft.ML.ImageAnalytics:
ML.NET is an open source and cross-platform machine learning framework for .NET.
Sample code referenced by the .NET documentation
Azure Stream Analytics
.NET Labs -- Show Me the Tips and Tricks and Code
CryptoNets is a demonstration of the use of Neural-Networks over data encrypted with Homomorphic Encryption. Homomorphic Encryptions allow performing operations such as addition and multiplication over data while it is encrypted. Therefore, it allows keeping data private while outsourcing computation (see here and here for more about Homomorphic Encryptions and its applications). This project demonstrates the use of Homomorphic Encryption for outsourcing neural-network predictions. The scenario in mind is a provider that would like to provide Prediction as a Service (PaaS) but the data for which predictions are needed may be private. This may be the case in fields such as health or finance. By using CryptoNets, the user of the service can encrypt their data using Homomorphic Encryption and send only the encrypted message to the service provider. Since Homomorphic Encryptions allow the provider to operate on the data while it is encrypted, the provider can make predictions using a pre-trained Neural-Network while the data remains encrypted throughout the process and finaly send the prediction to the user who can decrypt the results. During the process the service provider does not learn anything about the data that was used, the prediction that was made or any intermediate result since everything is encrypted throughout the process. This project uses the Simple Encrypted Arithmetic Library SEAL version 3.2.1 implementation of Homomorphic Encryption developed in Microsoft Research.