AI Engineering
Practical AI projects, experiments, tutorials, and applied research.
Earth, in a Bottle. A Robot, with Imagination. And an AI That “Knows Everything.”
Earth Models, World Models, and Global Models are three fast-growing “families” of AI systems that are often mentioned together—but they solve very different problems. This blog provides an accessible, research-oriented map of the landscape for readers…
Deep Learning Concepts for Beginners : Cooking Pasta Analogy
What does learning deep learning have in common with cooking pasta?
More than you might think.
In this article, I explain deep learning concepts for beginners using the familiar process of learning to cook pasta—preparing ingredients, tasting, adjusting,…
Deep Learning Concepts for Beginners : Basketball Player Analogy
What if understanding neural networks felt as natural as watching someone learn basketball?
In this short read, I explain how a neural network learns—using the simple, familiar journey of a player practicing shots, missing, adjusting, and improving….
A Beginner-Friendly Guide to Privacy in AI
What happens when your AI model is accurate… but not private?
Trust disappears.
In this blog, I show you how to build privacy into your models using simple concepts like epsilon, clipping, and federated learning. We even run…
A Beginner-Friendly Guide to Explainable AI (XAI)
Curious how AI models make decisions—or what goes on inside their “black box”?
In my latest blog, I break down explainable AI with hands-on Python examples, using real tools like SHAP, LIME, ELI5, and DALEX. Whether you’re…
How to Detect Hidden Bias in Your ML Model — A Step-by-Step Tutorial
Ever wondered how AI decides who gets hired or approved for a loan?
Spoiler: it sometimes inherits our biases.
In my latest blog, I uncover how bias sneaks into machine learning—and how we can fix it to make…
AI Engineering brings together practical AI projects, experiments, tutorials, and applied research notes on building real-world AI systems. The goal is to make AI engineering easier to understand through concrete examples, beginner-friendly explanations, and practical implementation notes.