TensorFlow.NET VS vega

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.

TensorFlow .Net

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

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 .

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

If you're pointing out that Tensorflow.NET is exciting (https://github.com/SciSharp/TensorFlow.NET), I agree!

Looks like TensorFlow is callable directly from .NET. https://github.com/SciSharp/TensorFlow.NET

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

Why don't you try this: https://www.nuget.org/packages/TensorFlow.NET/ (and maybe other nugets).

Hi HN – Excited to share a beta for Minard, a new data visualization toolkit we've been working on that lets you generate publication-quality charts with simple natural language (throw away your matplotlib docs and rejoice!).

Upload or import CSVs, Excel, and JSON, give it a spin, and please let us know what you think! (Long format data works best for now)

For those curious, the stack is a simple Django app with HTMX/Alpine and all of the charts are specified and rendered as Vega (https://vega.github.io/vega/). Lots of LLM function calling under the hood as well.

I recently added support for plotting XGBoost models using Vega (https://vega.github.io/vega/) into the XGBoost Elixir API (https://github.com/acalejos/exgboost).

Since EXGBoost supports loading trained models across different APIs, you can even train using the Python API and then plot using this Elixir API if you prefer.

The Data Source is from devjobsscanner (I am basically the owner, so I have the data) an the tool used to make the chart is Vega

It’s based on Vega https://vega.github.io/vega/ which means it’s an already matured backend. Vega-lite is the Javascript package and Altair is the Python.

Site:: https://vega.github.io/vega/

Eh, I have no reason to, and not much interest in PHP anymore. Things like Vega seem really cool.

This chart is make with Vega (and also all the charts from the article). Data source is from devjobsscanner and contains about 10 Million dev jobs offers, which only a small subset contain salary information thought.

Not that I'm working on it, but I'd love to see a vega stack in rust.