Day 158 — Building a CNN for Image Classification

• Assemble a complete CNN pipeline — from raw pixels to class predictions — using PyTorch

• Wire together Conv2d, BatchNorm, pooling, and fully connected layers into a production-grade architecture

• Train, evaluate, and export a model that mirrors what ships in real computer vision systems

Every camera-facing AI product you interact with — Google Photos tagging your vacation, Tesla’s Autopilot reading lane markings, Pinterest’s visual search matching your screenshots — runs on a CNN at its core. Yesterday you understood pooling and flattening in isolation. Today those pieces click together into a working classifier you can train end-to-end. This is the moment the theory stops being abstract. A student who completes today’s lesson walks away with a neural network architecture that, with more data and compute, could power a real production service.

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