This used binary and multiclass sequential neural networks to model the expected ESRB rating of video games using features related to game content.

This was a two-pronged foray into LSTMs and their capabilities in NLP. The BBC project was an attempt to categorize articles using a bidirectional LSTM, whereas the Nursery Rhymes project was an attempt to train a generative model that could write nursery rhymes using prompts.

An exploration of Deep-Q learning and it's applications for modeling game-playing agents; this project focused on training models to play Atari Breakout, Super Mario Bros., and Geometry Dash.

This explored both classification with convolutional neural networks as well as generative modeling with a C-VAE and a C-GAN using the Handwritten Numbers from the MNIST dataset.