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.

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: the Deep Learning from Scratch course, Convolutions from Scratch
Time: ~75 minutes

Objective

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, Check Yourself, Key Terms & Next — plus a hands-on lab, quiz, and project artifact.

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