Uncategorized

Adjusted R-squared is one of those metrics that shows up early in regression, but it often feels like a small […]

When you train a regression model, you usually want to answer a simple question: How well does this model explain

Logistic Regression is one of the simplest and most widely used building blocks in machine learning. In this article, we

DeepSeek V3.2 is one of the open-weight models that consistently competes with frontier proprietary systems (for example, GPT-5-class and Gemini

Imagine trying to understand a person’s life story just by looking at their credit card statements. You would see transactions—purchases,

Imagine you are reading a mystery novel. The clue you find on page 10 is crucial for understanding the twist

Imagine you have just built a high-performance race car engine (your Large Language Model). It is powerful, loud, and capable

Rotary Positional Embeddings represent a shift from viewing position as a static label to viewing it as a geometric relationship. By treating tokens as vectors rotating in high-dimensional space, we allow neural networks to understand that “King” is to “Queen” not just by their semantic meaning, but by their relative placement in the text.

In this article, we will explore two of the most popular, unsupervised algorithms for this task: RAKE (Rapid Automatic Keyword Extraction) and YAKE (Yet Another Keyword Extractor). We will start with intuitive explanations, then move into mathematical details, and finally look at practical Python implementations and best practices.

Designing an AI system often feels like choosing how to travel from Point A to Point B. The destination is fixed (your business outcome), but you can walk, drive, or fly to get there. This article is a practical compass to help you decide when to use rules, traditional ML, or Generative AI – and how to justify that choice.

For Large Language Models (LLMs), inference speed and efficiency are paramount. One of the most critical optimizations for speeding up text generation is KV-Caching (Key-Value Caching).

Introduction: The Quest to Understand Language Imagine a machine that could read, understand, and write text just like a human.