0.1 The Story Behind This Series
Imagine you’re teaching a robot to see. You show it a picture of your cat, but to the robot, this is just a giant grid of numbers! How do we help it understand what it’s looking at? That’s the beautiful challenge of computer vision.
This series follows the Pareto Principle: we’ll focus on the 20% of concepts and tools that give you 80% of the results. By the end, you’ll understand the journey from classical OpenCV techniques to modern foundation models like DINOv2.
0.2 Learning Philosophy
Following Jeremy Howard’s fastai approach: - Start with working code: See results first, understand theory later - Build intuition: Every concept explained with simple analogies - Learn by doing: Each post includes hands-on Colab notebooks - Progressive complexity: From pixels to neural networks, step by step
0.3 The Journey Ahead
0.3.1 🎯 Phase 1: Understanding Images
- Why Computer Vision? - The big picture and motivation
- Images as Data - How computers see the world
0.3.2 🔧 Phase 2: Classical Computer Vision
- OpenCV Essentials - Your first computer vision toolkit
- Finding Patterns - Edges, contours, and shapes
- Image Segmentation - Dividing images into meaningful parts
- Feature Magic - What makes images unique
0.3.3 🧠 Phase 3: Deep Learning Revolution
- Why Deep Learning? - When classical methods hit the wall
- Modern Vision Models - CNNs, Vision Transformers, and DINOv2
0.3.4 🚀 Phase 4: Building Your Future
- Your First CV Project - Putting it all together
- Where to Go Next - Your learning roadmap
0.4 What You’ll Build
By the end of this series, you’ll have: - Built 5+ hands-on projects (from edge detection to object classification) - Used both classical and modern techniques (OpenCV + PyTorch) - Extracted features with DINOv2 (foundation model magic) - Created your own image classifier (transfer learning)
0.5 Prerequisites
- Python basics: Variables, functions, loops (we’ll explain the rest!)
- Curiosity: That’s honestly the most important part
- Google account: For running our Colab notebooks
0.6 Tools We’ll Master
- OpenCV: The Swiss Army knife of classical computer vision
- NumPy & Matplotlib: For working with image data
- PyTorch: Modern deep learning framework
- HuggingFace: Pre-trained models made easy
- Google Colab: Free GPU power for everyone
0.7 How to Use This Series
- Read each post like a story (they’re designed to be fun!)
- Run the Colab notebooks (hands-on learning is key)
- Experiment with your own images (use photos of your pets, friends, anything!)
- Ask questions in the comments (no question is too basic)
0.8 A Personal Note
This series is designed for someone just starting their computer vision journey. I believe everyone can learn to teach computers to see – it just takes the right approach. We’ll go slow, explain everything, and have fun along the way.
Remember: every expert was once a beginner. Let’s begin this journey together!
Ready to jump in? Start with Why Computer Vision? and follow along with the Colab notebook. Each post builds on the previous one, so take your time and enjoy the process!
0.9 Getting Help
- 💬 Comments: Use the discussion section below each post
- 🐙 GitHub: Find all code in our repository
- 📧 Email: Reach out directly for specific questions
This series is continuously updated based on reader feedback and new developments in computer vision. Your questions and suggestions help make it better for everyone!
Reuse
Citation
@online{2026,
author = {, Hasan},
title = {Computer {Vision} {Foundations} {Series}},
date = {2026-08-23},
langid = {en}
}