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Yesterday, Prashanth Govindarajan posted an article about DataFrame on the .NET Blog. I got excited and wanted to try the library as soon as I could. In this post, I will explain what the library is intended for and what are my thoughts on it.
The package The DataFrame related classes were introduced in the package Microsoft.
These past months I realized that my blog, which is made using Wordpress, is very slow and that hurts the traffic. So I went looking for the problems and their solutions but nothing really worked, so I decided to switch everything.
Today I’ll talk about that decision, its advantages and disadvantages.
A few days ago I needed a way to connect to a server using a Socks5 proxy but couldn’t find an up-to-date implementation for .NET Core, so I decided to give it a go myself.
The implementation is pretty straightforward and easy, I got inspired from starksoft-aspen and followed the official RFC.
When I was looking for K-means use cases, I found out about Color quantization, a very interesting . I implemented it in Python and was wondering whether it would be as easy to implement in ML.NET.
All the code is available in this GitHub repository.
What is color quantization Color quantization is the usage of quantization, a lossy compression technique, in color spaces in order to reduce the number of unique colors in an image.
There is a lot of tutorials that show how to integrate Google Sign-In in your website, but only a handful show how to integrate it in a REST API.
Today I’ll be showing how you can add Google Sign-In and still manage users in your back-end.
Overview Here’s a sequence diagram showing how it all works:
I got interested in ASP.NET Core 3.0 since the first preview and followed it very closely. I started using it since preview2 and will now soon go to production with it (preview9).
I compiled this list to have all the new features, improvements and breaking changes that happened during this time for easy access and accessibility.