Think of a machine learning model as a high-performance engine prototype sitting on a pristine workbench. It might run beautifully […]
MLOps (Machine Learning Operations): From a Notebook to a Reliable Production System Read More »
There is an old proverb that perfectly captures the intuition behind one of the most fundamental algorithms in machine learning:
k-Nearest Neighbors (KNN): From Geometry to Algorithms Read More »
These terms are frequently used interchangeably, but they refer to different layers of software abstraction. Most confusion comes from accidentally
Software Abstractions: Library vs Package vs Framework vs Platform vs SDK vs Ecosystem Read More »
Machine Learning is often described as “data + algorithms”, but mathematics is the glue that makes everything work. At its
The Core Mathematical Foundations of Machine Learning Read More »
When you train a regression model, you usually want to answer a simple question: How well does this model explain
R-Squared (\(R^2\)) Explained: How To Interpret The Goodness Of Fit In Regression Models Read More »
Logistic Regression is one of the simplest and most widely used building blocks in machine learning. In this article, we
Logistic Regression in PyTorch: From Intuition to Implementation Read More »
Target encoding, also known as mean encoding or impact encoding, is a powerful feature engineering technique used to transform high-cardinality
Target Encoding: A Comprehensive Guide Read More »
Extremely Randomized Trees (Extra-Trees) is a machine learning ensemble method that builds upon Random Forests construction process. Unlike Random Forests,
Understanding Extra-Trees: A Faster Alternative to Random Forests Read More »
Imagine building a city: at first, you lay simple roads and bridges, but as the population grows and needs diversify,
How Large Language Model Architectures Have Evolved Since 2017 Read More »
Imagine you’re teaching a robot to write poetry. You give it a prompt, and it generates a poem. But how
How to Evaluate Text Generation: BLEU and ROUGE Explained with Examples Read More »
Imagine you’re a master chef. You wouldn’t just throw ingredients into a pot; you’d meticulously craft a recipe, organize your
From Prompts to Production: The MLOps Guide to Prompt Life-Cycle Read More »
Imagine a master chef. This chef has spent years learning the fundamentals of cooking—how flavors combine, the science of heat,
The Ultimate Guide to Customizing LLMs: Training, Fine-Tuning, and Prompting Read More »
