infer
ML.NET
infer | ML.NET | |
---|---|---|
3 | 17 | |
1,540 | 8,855 | |
0.4% | 0.6% | |
6.7 | 8.9 | |
2 months ago | 6 days ago | |
C# | C# | |
MIT License | MIT License |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
infer
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Rxinfer: Automatic Bayesian Inference Through Reactive Message Passing
A closer library is Infer.NET: https://dotnet.github.io/infer
It includes a really mature compiler that generates very efficient message passing and variational inference, with support for online inference, which is the main focus on Rxinfer.
You can call Infer.NET from Python in a number of ways, despite it is not a CPython library.
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how to execute infer.net tutorial examples on linux?
Hi, I am trying to run infer.net tutorial browser examples without any .net programming experience. I have successfully built the source code from command line.I tried run "dotnet run" on tutorial folder but wasn't successful.
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Trying to run python.NET but haven't been successful
I am trying to infer.net python example on manjaro linux. I keep getting this error:
ML.NET
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ML.net image classification, poor GPU accuracy
You can direct your question to https://github.com/dotnet/machinelearning/issues. Perhaps it is already documented.
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Building a File Analysis Dataset with Python
Here I'm analyzing all projects in the src and test directories of the ML.NET repository. I chose to include these as separate paths because they represent two different groupings of projects in this repository.
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Extracting git repository data with PyDriller
Important Note: looping over repository commits takes a long time for large repositories. It took 52 minutes to analyze the ML.NET repository this code example refers to, which had 2,681 commits at the time of analysis on February 25th, 2023.
- Can we please be allowed to do machine learning object detection model training locally?
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ML.NET: can Microsoft's machine learning be trusted?
We checked the ML.NET 1.7.1 version. The source code of this project's version is available on GitHub.
- Stable Diffusion converted to ONNX (Demo usage, optimized to CPU)
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Why is there a lack of cool repos?
machine learning? https://github.com/dotnet/machinelearning
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what is the future of ML.NET?
You can follow some of our plans by taking a look at our roadmap which we'll be updating shortly to more accurately reflect the areas we're investing in.
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Does anyone actually use ML.NET?
Re: ONNX, if you run into similar issues in the future, feel free to reach out in our GitHub repo or the ONNX Runtime repo and we'd be happy to help!
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Requesting Senior Project Ideas
Good clarification, I think using something like ML.NET could be cool but I have some experience with Blazor that might be fun to use as well, I think generally performance monitoring or optimizing systems seems interesting to me, and I'm really open to other ideas as well. Let me know if any of that helps narrow my question down!
What are some alternatives?
TensorFlowSharp - TensorFlow API for .NET languages
TensorFlow.NET - .NET Standard bindings for Google's TensorFlow for developing, training and deploying Machine Learning models in C# and F#.
PythonNet - Python for .NET is a package that gives Python programmers nearly seamless integration with the .NET Common Language Runtime (CLR) and provides a powerful application scripting tool for .NET developers.
Accord.NET
FaceRecognitionDotNet - The world's simplest facial recognition api for .NET on Windows, MacOS and Linux
WaveFunctionCollapse - Bitmap & tilemap generation from a single example with the help of ideas from quantum mechanics
OpenCvSharp - OpenCV wrapper for .NET
Catalyst - 🚀 Catalyst is a C# Natural Language Processing library built for speed. Inspired by spaCy's design, it brings pre-trained models, out-of-the box support for training word and document embeddings, and flexible entity recognition models.
Deedle - Easy to use .NET library for data and time series manipulation and for scientific programming
AForge.NET - AForge.NET Framework is a C# framework designed for developers and researchers in the fields of Computer Vision and Artificial Intelligence - image processing, neural networks, genetic algorithms, machine learning, robotics, etc.
Synapses - A group of neural-network libraries for functional and mainstream languages