Phase 1: Convolutional Neural Networks · ~75 minutes · Python

Image Generation — GANs

A GAN is two neural networks in a fixed game. One draws, one critiques. They get better together until the drawings fool the critic.

Hiring signal: Understanding of image generation — gans internals

What you will learn

Explain the minimax game between generator and discriminator and why the equilibrium corresponds to p_model = p_data
Implement a DCGAN in PyTorch and get it to generate coherent 32x32 synthetic images in under 60 lines
Stabilise GAN training with the three standard tricks: non-saturating loss, spectral norm, TTUR (two-timescale update rule)
Read training curves that distinguish healthy convergence from mode collapse, oscillation, and discriminator-wins-completely

Introduction

Type: Build Languages: Python Prerequisites: Phase 4 Lesson 03 (CNNs), Phase 3 Lesson 06 (Optimizers), Phase 3 Lesson 07 (Regularization) Time: ~75 minutes

Learning Objectives

Explain the minimax game between generator and discriminator and why the equilibrium corresponds to p_model = p_data
Implement a DCGAN in PyTorch and get it to generate coherent 32x32 synthetic images in under 60 lines
Stabilise GAN training with the three standard tricks: non-saturating loss, spectral norm, TTUR (two-timescale update rule)
Read training curves that distinguish healthy convergence from mode collapse, oscillation, and discriminator-wins-completely

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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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