Phase 0: Vision Foundations · ~75 minutes · Python

CNNs — LeNet to ResNet

Every major CNN of the last thirty years is the same conv–nonlinearity–downsample recipe with one new idea bolted on. Learn the ideas in order.

Hiring signal: Understanding of cnns — lenet to resnet internals

What you will learn

Trace the architectural lineage LeNet-5 -> AlexNet -> VGG -> Inception -> ResNet and state the single new idea each family contributed
Implement LeNet-5, a VGG-style block, and a ResNet BasicBlock in PyTorch, each under 40 lines
Explain why residual connections turn a 1,000-layer network from untrainable into state-of-the-art
Read a modern backbone (ResNet-18, ResNet-50) and predict its output shape, receptive field, and parameter count before looking at the source

Introduction

Type: Learn + Build Languages: Python Prerequisites: Phase 3 Lesson 11 (PyTorch), Phase 4 Lesson 01 (Image Fundamentals), Phase 4 Lesson 02 (Convolutions from Scratch) Time: ~75 minutes

Learning Objectives

Trace the architectural lineage LeNet-5 -> AlexNet -> VGG -> Inception -> ResNet and state the single new idea each family contributed
Implement LeNet-5, a VGG-style block, and a ResNet BasicBlock in PyTorch, each under 40 lines
Explain why residual connections turn a 1,000-layer network from untrainable into state-of-the-art
Read a modern backbone (ResNet-18, ResNet-50) and predict its output shape, receptive field, and parameter count before looking at the source

Unlock the full lesson

You've read the first 2 sections. The rest of this lesson covers The Problem, The Concept, Build It, Use It, Ship It, Exercises, Key Terms, Further Reading — plus a hands-on lab, quiz, and project artifact.

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