A subfield of artificial intelligence called machine learning (ML) gives computers the ability to learn from data and predict the future. ML can be used in a variety of fields, including recommender systems, natural language processing, computer vision, etc. However, doing so can be difficult, especially for developers who are not experienced with ML frameworks and tools. The 10 tools described in this post will make it simple and effective for you to include machine learning in your .NET projects.
ML.NET
A cross-platform, open-source machine learning framework for.NET developers is called ML.NET. You can use C# or F# to design, develop, and deploy your ML models. To make the ML development process simpler, it also offers several components and tools, like AutoML, Model Builder, ML.NET CLI, etc.
Microsoft Cognitive Toolkit
Microsoft Research created the deep learning framework known as CNTK. It provides powerful APIs for building and training neural networks, supports CPU and GPU processing, and is cross-platform. From C#, Python, or C++, you can utilize CNTK.
TensorFlow.NET
The most well-known deep learning framework in the world, TensorFlow, has a.NET binding called TensorFlow.NET. You may access all of TensorFlow’s capabilities and functionalities by using it from C# or F#.
SciSharp STACK
A collection of open-source.NET libraries for scientific computing and machine learning is called SciSharp STACK. It includes Torch.NET, Keras.NET, TensorFlow.NET, NumSharp.NET, and so forth. These libraries can be used to carry out several tasks, including data manipulation, linear algebra, neural network modeling, etc.
Accord.NET
The.NET machine learning framework Accord.NET offers methods for image processing, computer vision, clustering, classification, regression, audio processing, etc. Additionally, libraries for arithmetic, statistics, signal processing, etc. are included.
Deedle
A.NET library for data exploration and analysis is called Deedle. It offers data structures and operations for utilizing time-series and structured data. Data can be manipulated, aggregated, filtered, transformed, visualized, and exported using Deedle.
F# Data
A.NET package called F# Data facilitates effective and simple use of data. For working with several types of data sources, like CSV, JSON, XML, HTML, etc., type providers are provided. To parse, query, process, and validate data, utilize F# Data.
Windows Azure Machine Learning
Building, training, deploying, and managing ML models at scale are all made possible by the cloud-based platform known as Azure Machine Learning. Through REST APIs or SDKs, Azure Machine Learning is accessible from any.NET language. For building ML pipelines, you may also use the web-based drag-and-drop tool Azure Machine Learning Studio.
Windows ML Server
A server-based framework called ML Server enables you to run R and Python scripts locally or in the cloud. You can use ML Server to carry out data analysis, statistical modeling, machine learning, and other tasks using a variety of packages and frameworks. Web services or APIs can be used to integrate ML Server with.NET applications.
Microsoft Infer.NET
A.NET framework for probabilistic programming is called Infer. NET. It helps you to develop models that account for data variability and uncertainty, and it does inference using a range of algorithms. For tasks like Bayesian inference, graphical models, latent variable models, etc., you can utilize Infer. NET.
