Phase 0: Vision Foundations · ~45 minutes · Python
Image Fundamentals — Pixels, Channels, Color Spaces
An image is a tensor of light samples. Every vision model you will ever use starts from this one fact.
What you will learn
- Explain how a continuous scene gets discretized into pixels and why sampling/quantization decisions set the ceiling on every downstream model
- Read, slice, and inspect images as NumPy arrays and switch fluently between HWC and CHW layouts
- Convert between RGB, grayscale, HSV, and YCbCr and justify why each color space exists
- Apply pixel-level preprocessing (normalize, standardize, resize, channel-first) exactly as torchvision expects it
Introduction
- Type: Build
- Languages: Python
- Prerequisites: the Math for AI & ML course, the Deep Learning from Scratch course, Tensor Operations
- Time: ~45 minutes
Objective
Learning objectives
- Explain how a continuous scene gets discretized into pixels and why sampling/quantization decisions set the ceiling on every downstream model
- Read, slice, and inspect images as NumPy arrays and switch fluently between HWC and CHW layouts
- Convert between RGB, grayscale, HSV, and YCbCr and justify why each color space exists
- Apply pixel-level preprocessing (normalize, standardize, resize, channel-first) exactly as torchvision expects it
Unlock the full lesson
You've read the first 2 sections. The rest of this lesson covers The Problem, The Concept, Build, Check Yourself, Key Terms & Next — plus a hands-on lab, quiz, and project artifact.
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