Module 8PyTorch Foundations

Convolutional Networks

Understand receptive fields, conv blocks, and residual design before using ResNet as a black box.

Why this module matters

CNNs remain one of the clearest ways to learn feature extraction and architecture design.

Prerequisites

  • ▸ Training loops
  • ▸ GPU basics

Learning objectives

  • ▸ Compute feature map shapes correctly
  • ▸ Design conv blocks and residual shortcuts
  • ▸ Compare shallow and deep CNNs

Core concepts

Convolution and padding
Pooling and receptive field growth
Residual learning

Hands-on practice

  • ▸ Build a CIFAR-style CNN
  • ▸ Add residual shortcuts
  • ▸ Compare validation accuracy with and without augmentation

Expected output

A CNN baseline and an improved residual version for CIFAR-10.

Study checklist

  • ✅ Compute feature map shapes correctly
  • ✅ Design conv blocks and residual shortcuts
  • ✅ Compare shallow and deep CNNs

Common mistakes

  • ⚠️ Using ImageNet-style maxpool on tiny 32x32 images
  • ⚠️ Losing track of feature map shapes
  • ⚠️ Overfitting before checking augmentation and regularization

Module rhythm

  • 1. Read the summary and why-it-matters section first.
  • 2. Work through concepts before rushing into practice.
  • 3. Use the checklist to verify real understanding, not just completion.

How to continue

This sets up the final capstone, where you assemble the full CNN training pipeline end to end.

Back to course overview →

How to use this page well

Treat each module as a compact learning system: understand the intuition, verify the concepts, do one hands-on task, then use the checklist and mistakes section to pressure-test your understanding.