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.
03 October / / ASP.NET Core
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:
13 September / / ASP.NET Core
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.
Patrick Smacchia, the author of NDepend, offered me a license for NDepend and I was thrilled, as I was always interested in it. In this blog post, I'll be talking about my opinion and thoughts after trying it for the first time. Static code analysis Being able to check the quality of your source code before it's even ran is a very valuable thing to have.
In parts #1 and #2 of the “Outliers Detection in PySpark” series, I talked about Anomaly Detection, Outliers Detection and the interquartile range (boxplot) method. In this third and last part, I will talk about how one can use the popular K-means clustering algorithm to detect outliers. K-means K-means is one of the easiest and most popular unsupervised algorithms in Machine Learning for Clustering.
In the first part, I talked about what Data Quality, Anomaly Detection and Outliers Detection are and what’s the difference between outliers detection and novelty detection. In this part, I will talk about a very known and easy method to detect outliers called Interquartile Range. Introduction The Interquartile Range method, also known as IQR, was developed by John Widler Turky, an American mathematician best known for development of the FFT algorithm and box plot.