From Noise to Numbers: Building a DCGAN for MNIST Generation Using PyTorch

Author(s): Souradip Pal

Originally published on Towards AI.

Imagine a neural network dreaming up handwritten digits so real, they fool even trained eyes — or sketching fashion items never seen before. This isn’t sci-fi. It’s the magic of Generative Adversarial Networks.

First proposed by Ian Goodfellow in 2014, GANs sparked a revolution in synthetic data creation. These dual-network systems — one generating data, the other critiquing it — compete and collaborate in a digital dance until what’s fake looks convincingly real.

But how do they actually work? And more importantly, how can you build one from scratch?

In this hands-on guide, you’ll go beyond theory. You’ll train your very own Deep Convolutional GAN (DCGAN) using PyTorch. We’ll generate handwritten digits and fashion images using real-world datasets curated by Hugging Face.

The architecture? We’ll walk through it, block by block.

The training process? You’ll watch the generator get better with every epoch, learning how to trick its rival into believing it’s created something real.

And by the end, you won’t just understand how DCGANs operate — you’ll have built one that learns to imagine.

We’ll keep the code minimal, the logic crystal clear, and the explanations visual and digestible. Whether you’re dipping your toes into generative modeling or deep-diving as a seasoned AI dev, this tutorial… Read the full blog for free on Medium.

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Published via Towards AI