The Core Mathematical Foundations of Machine Learning
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 »
R-Squared (\(R^2\)) Explained: How To Interpret The Goodness Of Fit In Regression Models
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 in PyTorch: From Intuition to Implementation
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: A Comprehensive Guide
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 »
Understanding Extra-Trees: A Faster Alternative to Random Forests
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 »
How Large Language Model Architectures Have Evolved Since 2017
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 »
How to Evaluate Text Generation: BLEU and ROUGE Explained with Examples
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 »
From Prompts to Production: The MLOps Guide to Prompt Life-Cycle
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 »
The Ultimate Guide to Customizing LLMs: Training, Fine-Tuning, and Prompting
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 »
Understanding PEFT: A Deep Dive into LoRA, Adapters, and Prompt Tuning
Imagine you’re trying to teach a world-class chef a new recipe. Instead of retraining them from scratch, you just show
Understanding PEFT: A Deep Dive into LoRA, Adapters, and Prompt Tuning Read More »
Explainable AI: Driving Transparency And Trust In AI-Powered Solutions
AI systems are becoming integral to our daily lives. However, the increasing complexity of many AI models, particularly deep learning,
Explainable AI: Driving Transparency And Trust In AI-Powered Solutions Read More »
What is Batch Normalization and Why is it Important?
Batch normalization was introduced in 2015. By normalizing layer inputs, batch normalization helps to stabilize and accelerate the training process,
What is Batch Normalization and Why is it Important? Read More »