This repository is made as supplementary material for a tutorial. The tutorial shows how to use Recurrent Neural Nets as generative models.

GAN to generate digits in MNIST dataset

Generated digits (Similar to the ones in the MNIST dataset) using Wasserstein GANs.

This is a digit generation project that employs Generative Adversarial Networks (GANs) to generate realistic handwritten digits.

A machine learning project that repurposes Bernoulli Naive Bayes as a generative model to synthesize handwritten digits from the MNIST dataset. Implements pixel-wise probability learning, sampling, and image generation with smoothing techniques.