Projects

This project trains a generative adversarial network on MNIST, then exports the trained generator weights so the portfolio can reconstruct the model in the browser.

The browser demo runs a fully connected generator that maps a 128-dimensional noise vector through linear layers with batch normalization and LeakyReLU activations before producing a 784-value (28x28) MNIST image. The model uses a final Tanh output and contains 2,449,680 trainable parameters.