$ npm install
$ next build --turbo
$ starting aligulum.dev
Writing
Mostly machine learning and .NET — algorithms, ML.NET, and the math underneath.
LLMs aren't always the answer. Learn why engineers over-engineer with AI when classical ML models are faster, cheaper, and more accurate for most real-world problems.
Part 3 of the series: a Windows Forms app that fetches real reviews and color-codes them using the trained ML.NET sentiment model.
Part 2 of the series: a console app that prepares the IMDB dataset and trains the sentiment model built in Part 1.
Part 1 of a three-part series: training an ML.NET model to classify IMDB movie reviews as positive or negative.
How the Naive Bayes algorithm works, why it performs surprisingly well despite a naive assumption, and a from-scratch C# implementation for text classification.
How the K-Nearest Neighbor algorithm works, and a from-scratch C# implementation used to power an article recommendation example.
A short overview of the distance metrics, Euclidean, Manhattan, and Cosine, that power algorithms like K-Nearest Neighbor and clustering.
A refresher on the matrix operations, addition, multiplication, inverse, transposition, that most machine learning algorithms rely on under the hood.
An overview of ML.NET, the open-source machine learning framework that lets .NET developers build and ship custom models in C# or F#.
Machine learning algorithms power more of daily life than most people realize. This article breaks down how they work, how they differ, and where the most widely used ones actually show up in the real world.
