Series · 6 posts Contact

Building AI Model.
From scratch.

6 posts · Beginner-friendly · Code Included Quizzes Diagrams

This series is for builders who want to go beyond using AI to actually building it. You'll understand how models are trained, how generative AI works, and how to ship AI systems into production. No PhD required — just curiosity and a code editor.

Hands-on Code

Every concept comes with working Python code snippets you can run immediately.

Visual Diagrams

Architecture diagrams and data-flow visualizations for every major concept.

Quizzes

Knowledge checks and mini-projects to reinforce learning at the end of each post.

Beginner-Friendly

Real-world analogies throughout. We assume curiosity, not a math background.

All Posts

Start from Post 01 and work your way through — each builds on the last.

How AI Models Are Built Live ●

Training pipelines, datasets, loss functions, and gradient descent — the core mechanics behind every AI model, explained from scratch.

Training Gradient Descent Backprop

Generative AI Fundamentals Live ●

VAEs, GANs, Diffusion Models — how machines learn to generate images, text, and audio. We walk through each architecture with diagrams and code.

GANs Diffusion VAE

Working with LLMs Live ●

Fine-tuning, prompt engineering, RAG, and embedding search — practical techniques for building production-grade LLM applications.

LLMs RAG Fine-tuning

Building a Chatbot Live ●

End-to-end walkthrough: from your first API call to adding memory, tool use, and a production deployment. Full working code included.

Chatbot Memory Tool Use

Image Generation Pipeline Live ●

Stable Diffusion internals, ControlNet, and LoRA fine-tuning — build and customize your own image generation model from the ground up.

Stable Diffusion LoRA ControlNet

Deploying AI to Production Live ●

Model serving, latency optimization, cost control, and monitoring — everything you need to run AI reliably at scale in production.

MLOps Serving Scaling