Portfolio Project

Synthetic Digit Generator

Variational Autoencoder

Machine Learning Python VAE AWS Docker

Generate a grid of synthetic digits and explore how the output morphs.

  • Choose a “Number” (or Auto) to sample from.
  • Click “Generate” to refresh the grid.
  • Open “Extra settings” to adjust seed, dim, grid density, and distortion level.
  • Generate again to compare how settings change the outputs.

STAR Summary

Situation
I wanted to generate new handwritten digits, not just recognize them.
Task
Owned the end-to-end build, from implementation through the final deliverable.
Action
  • Trained a Variational Autoencoder (VAE) on MNIST (60,000 training digits) with a 20-dim latent space for up to 100 epochs.
Result
  • Generated new digits by sampling the learned latent space.
  • Saved the trained model so generation is a quick inference step.