Back to Education
Book · Machine Learning with PyTorch and Scikit-Learn

Generative Adversarial Networks in PyTorch

A generative modeling milestone progressing from a simple GAN to DCGAN and WGAN-GP for more stable synthetic image generation.

GANDCGANWGAN-GPGenerator vs discriminatorLatent space
GitHub

What this project is about

The GAN milestone studies adversarial generation: generator vs discriminator, DCGAN convolutional generation, and WGAN-GP for more stable training.

Implementation focus

This gave me a deeper view of generative modeling before LLMs: generation can be trained through competition, not only next-token prediction.

Key results and artifacts

  • Simple GAN latent dimension: 20
  • DCGAN uses ConvTranspose2D generator blocks
  • WGAN-GP adds critic logic and gradient penalty

What I learned

The important takeaway was adversarial training: one network generates while another network pressures it toward realism.

Milestone deep dive

What this project proves in my learning path.

A generative modeling milestone progressing from a simple GAN to DCGAN and WGAN-GP for more stable synthetic image generation. The milestone is included because it shows a complete learning unit: a problem, a data representation, an implementation path, measurable outputs, and a lesson that later connects to larger AI systems.

Input layercourse dataset, features, labels, or raw input artifacts
Model layerimplemented algorithm or neural network pipeline
Evaluation layertests, metrics, plots, or qualitative output
Artifact layermetrics, plots, predictions, and visual evidence
Problem framing

What I was trying to solve

This page is not just a lab description. It explains how I transformed the assignment idea into an engineering story: what the model receives, what it optimizes, what output it should produce, and how the result can be inspected.

Implementation

What I coded or assembled

The implementation focus was the mechanism behind the result: functions, model blocks, training logic, preprocessing, evaluation, and the small technical details that make the notebook work rather than only look correct.

Concepts

Core concepts

GANDCGANWGAN-GPGenerator vs discriminatorLatent space

These tags show the technical ideas this milestone added to my toolkit.

Learning result

What changed after it

The important takeaway was adversarial training: one network generates while another network pressures it toward realism.

Important outputs and evidence

  • Simple GAN latent dimension: 20
  • DCGAN uses ConvTranspose2D generator blocks
  • WGAN-GP adds critic logic and gradient penalty

How I would explain it in an interview

I would describe this milestone by starting from the data representation, then explain the model or algorithm, then show what was measured. The important part is not only the final number; it is the ability to reason about why the method works, what assumptions it makes, and what limitations should be improved next.