If you’ve ever peeked inside a machine learning model—or even trained one—you’ve probably heard the phrase “gradient descent.” But what is it exactly? And why should engineers care? Today, let’s strip away the jargon and look at gradient descent in plain English. No equations, just clear thinking — and a link to the world of […]
Tag: machine learning
Why Every Engineer Should Understand the Basics of Machine Learning
Whether you’re building back-end systems, mobile apps, or front-end features, one thing is becoming clear: machine learning (ML) is no longer just for data scientists—it’s becoming part of a modern engineer’s toolbox. But why should every engineer care? 1. Models Are Just Supercharged If-Else Statements Imagine you’re writing code to categorise emails as “spam” or […]
Calculus for Machine Learning – A Practical Guide
🎯 Goal: To give you a solid intuition for how and why calculus (especially derivatives and gradients) powers machine learning learning & optimisation. No need to master every detail — just enough to understand what’s going on when a model trains. 🧮 1️⃣ What’s Calculus Doing in ML? Calculus is all about change. In ML, […]
Linear Algebra for Machine Learning
🎯 Goal: Help you understand and use vectors, matrices, and dot products — the building blocks behind ML models like linear regression, neural networks, and PCA. 🧱 1. What’s a Vector? ✅ Definition: A vector is just an ordered list of numbers — like a row or a column. In ML: 🧰 2. What’s a […]