The Efficiency Revolution: How to Choose the Right-Sized AI Model for Your Needs
Executive Summary As AI adoption accelerates, a critical shift is occurring: organizations are moving from…
Understanding the Layers of Large Language Models (LLMs) and How Data Passes Through Them
Large Language Models (LLMs) such as GPT (Generative Pre-trained Transformer) are a class of deep learning models that have revolutionized natural language processing (NLP).
How NVIDIA Graphics Work: A Comprehensive Guide to GPUs
Graphics Processing Units (GPUs) have become the backbone of modern computing, powering everything from gaming to artificial intelligence (AI).
How Data Transfer Takes Place from RAM to SSD: A Detailed Insight
In modern computing, the seamless transfer of data between various hardware components is crucial for maintaining system performance and efficiency.
Cryptocurrency: Understanding How It Works and Its Impact on the Financial World
Cryptocurrency has taken the world by storm, evolving from a niche concept into a mainstream financial asset class.
Let’s break down AI, Machine Learning (ML), and Neural Networks in a structured way
Let’s break down AI, Machine Learning (ML), and Neural Networks in a structured way, covering key concepts, types of ML, and model architectures like Transformers, and their applications.
Complete Breakdown of Machine Learning (ML)
Machine Learning
is a vast and intricate field that requires an understanding of key concepts from mathematics, statistics, programming, and data science. Let’s go through everything step-by-step, from the fundamental maths to the essential skills required to build ML models.
Partial Differential Equations (PDEs), Their Canonical Forms wave, heat, and Laplace equations
Partial differential equations (PDEs) are classified into different types based on their characteristics, which determine the nature of their solutions and the appropriate solution methods. The three most important PDEs in mathematical physics are: Partial differential equations (PDEs) are used in machine learning (ML)—especially in advanced fields like: Physics-informed neural networks (PINNs): These use PDEs…
Understanding Tensors: A Comprehensive Guide with Mathematical Examples
Welcome to our mathematically rigorous exploration of tensors. This guide provides precise definitions, theoretical foundations, and worked examples ranging from elementary to advanced levels. All concepts are presented using proper mathematical notation with no reliance on programming languages. The Story of Tensors: From Curved Surfaces to Cosmic Equations Long ago, in the 19th century, a…
Apple WWDC 2025: A Unified Vision with Liquid Glass & AI-Powered Ecosystems
At WWDC 2025, Apple unveiled its most ambitious software overhaul in years—Liquid Glass, a cohesive design language spanning all platforms, and Apple Intelligence, a privacy-first AI framework that enhances productivity, creativity, and communication. This year’s keynote wasn’t just about incremental updates; it was a foundational shift toward a more unified, intelligent, and visually refined ecosystem. From…
Everything You Need to Know to Build a Large Language Model (LLM) from Scratch: Architecture, Tokenization, Training & Deployment
What Are LLMs? LLMs are machine learning models trained on vast amounts of text data. They use transformer architectures, a neural network design introduced in the paper “Attention Is All You Need”. Transformers excel at capturing context and relationships within data, making them ideal for natural language tasks. 1. Architectural Types of Language Models (Expanded with…
The Epic Saga of the Cubic Equation: From Blood Feuds to Modern Algebra
The Drama of the Cubic Equation: Rivalries, Betrayals, and Renaissance Mathematics The 16th century was a time of mathematical duels, secret solutions, and bitter rivalries. The quest to solve the cubic equation wasn’t just about algebra—it was about fame, survival, and revenge. 1. Tartaglia: The Stuttering Genius Who Outsmarted His Rivals Niccolò Tartaglia (1500–1557) was…
Every Model in Machine Learning (Supervised, Unsupervised, Regression) explained
What is artificial intelligence? Artificial intelligence is a field of science concerned with building computers and machines that can reason, learn, and act in such a way that would normally require human intelligence or that involves data whose scale exceeds what humans can analyze. AI is a large field that includes many disciplines including computer…
How do LLMs work from tokenization, embedding, QKV Activation Functions to output
Course Introduction: How Large Language Models (LLMs) Work What You Will Learn: The LLM Processing Pipeline In this course, you will learn how Large Language Models (LLMs) process text step by step, transforming raw input into intelligent predictions. Here’s a visual overview of the journey your words take through an LLM: Module Roadmap You will…
Tiny Agents in Python: Build an MCP-Powered AI Assistant in <100 Lines
Introduction to MCP and Tiny Agents The Model Context Protocol (MCP) is revolutionizing how we build AI applications by standardizing tool integration for LLMs. In this guide, I’ll show you how to create a fully functional Python agent that leverages MCP to dynamically discover and use tools – all in under 100 lines of code….
The Efficiency Revolution: How to Choose the Right-Sized AI Model for Your Needs
Executive Summary As AI adoption accelerates, a critical shift is occurring: organizations are moving from “bigger is better” to “right-sized is smarter.” Our comprehensive analysis of 9 leading models across climate, economic, and healthcare domains reveals: Smaller models (3B-32B parameters) can match or exceed larger models’ accuracy on specialized tasks while using 24x less energy Newer model…
KV Caching Explained: A Deep Dive into Optimizing Transformer Inference
Introduction to KV Caching When large language models (LLMs) generate text autoregressively, they perform redundant computations by reprocessing the same tokens repeatedly. Key-Value (KV) Caching solves this by storing intermediate attention states, dramatically improving inference speed – often by 5x or more in practice. In this comprehensive guide, we’ll: Explain the transformer attention bottleneck Implement KV caching from scratch…
