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.
Hiring signal: Understanding of image fundamentals — pixels, channels, color spaces internals
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: Phase 1 Lesson 12 (Tensor Operations), Phase 3 Lesson 11 (Intro to PyTorch) Time: ~45 minutes
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
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