F# Data
ML.NET
F# Data | ML.NET | |
---|---|---|
6 | 17 | |
805 | 8,846 | |
0.6% | 0.5% | |
6.8 | 8.9 | |
14 days ago | 3 days ago | |
F# | C# | |
GNU General Public License v3.0 or later | 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.
F# Data
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A new Excel add-in for custom functions and much more using F#. A replacement for VBA?
It is a brilliant tool for being able to import and export data in almost any format from your local computer, a database or across the web. For common data formats like csv, xml, and json, the Type Providers in FSharp.Data allow you automatically generate a type safe API against a sample file.
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Why learn Racket? A student's perspective
F# has this kind of stuff also, where it will dynamically type data dynamic sources like databases or rest calls, etc. for an example https://fsprojects.github.io/FSharp.Data/
- Twenty Years of C# with Anders Hejlsberg [audio]
- FSharp.Data: Data Access Made Simple
- FSharp.Data 4.1.1 Released
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F# Async Webrequest?
Have a look at the FSharp.Data library. It has bunch of cool stuff including Http.
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?
Deedle - Easy to use .NET library for data and time series manipulation and for scientific programming
TensorFlow.NET - .NET Standard bindings for Google's TensorFlow for developing, training and deploying Machine Learning models in C# and F#.
Accord.NET
R Provider - Access R packages from F#
FaceRecognitionDotNet - The world's simplest facial recognition api for .NET on Windows, MacOS and Linux
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.
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.
numl - Machine Learning for .NET