TwitchJerpBot
TensorFlow.NET
| TwitchJerpBot | TensorFlow.NET | |
|---|---|---|
| 1 | 18 | |
| 1 | 3,123 | |
| - | 1.1% | |
| 6.3 | 8.6 | |
| 4 months ago | 17 days ago | |
| C# | C# | |
| GNU General Public License v3.0 or later | Apache License 2.0 |
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TwitchJerpBot
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I enjoy setting up the stream rather than streaming itself.
Code-side, C# is probably my overall favorite language (wrote my chat bot and some other tools in that, and have written a few tools for work as well), though I've dipped into C++ for debugging & smaller additions at work.
TensorFlow.NET
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Sentiment analysis in c#
But to answer your question. I've run UNet in C#. I trained the data originally using python and used SciSharp to run the model using GPU for a solution more than 5 years ago.
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AI .NET
TensorFlow .Net
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The Ultimate Guide to Tech Stack for Indie Hackers in 2023
Yes, exactly. Since you will probably use scikit / tensorflow / pytorch with Python, you can call your model directly in a Django controller. Using other frameworks you will probably have to create a separate microservice with a model exposed via Rest API or use bindings like TensorFlow.NET
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Have anyone use ML Net for production?
Not even close , It is close to python's scikit learn , and it does not have it is own deep learning framework like tensorflow or pytorch , while there are some developers that are trying to implement tensorflow in dotnet , they are doing a good job at providing TensorFlow's low-level C++ API , However c# is not as good as python in manipulating data , there are tons of online materials for python's data science libraries like numpy , pandas , scikit learn , tensorflow , pytorch , quick bug fixes , even some support for unique cases .
- If you had to pick a library from another language (Rust, JS, etc.) that isn’t currently available in Python and have it instantly converted into Python for you to use, what would it be?
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Does anyone have any experience using ML.NET for forecasting?
But Ive actually been looking at TensorFlow.NET. Its uses .NET binding for TensorFlow machine learning library. Heres the GitHub page: https://github.com/SciSharp/TensorFlow.NET
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Does Microsoft use F# for any of its internal projects?
If you're pointing out that Tensorflow.NET is exciting (https://github.com/SciSharp/TensorFlow.NET), I agree!
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Is F# a viable language for machine learning akin to Python?
Looks like TensorFlow is callable directly from .NET. https://github.com/SciSharp/TensorFlow.NET
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Need help Understanding A Neural Net better [P]
Building neural nets from scratch is going to be really hard, for a lot of reasons that you're not even aware of yet. You're better off using preexisting machine learning frameworks. For example here's a library for using tensorflow in c#: https://github.com/SciSharp/TensorFlow.NET
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How to develop an applications with multiple programming language
Why don't you try this: https://www.nuget.org/packages/TensorFlow.NET/ (and maybe other nugets).
What are some alternatives?
Unity-Twitch-Chat - Lightweight and easy-to-use Twitch IRC client for Unity.
ML.NET - ML.NET is an open source and cross-platform machine learning framework for .NET.
ComfyJS - Comfiest Twitch Chat Library for JavaScript | NodeJS + Browser Support
TorchSharp - A .NET library that provides access to the library that powers PyTorch.
clockify-nlp-bot - An NLP driven bot for interacting with Clockify timesheet via different channels
TensorFlowSharp - TensorFlow API for .NET languages
Accord.NET
NumSharp - High Performance Computation for N-D Tensors in .NET, similar API to NumPy.
m2cgen - Transform ML models into a native code (Java, C, Python, Go, JavaScript, Visual Basic, C#, R, PowerShell, PHP, Dart, Haskell, Ruby, F#, Rust) with zero dependencies
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.
encog-dotnet-core
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.