While working for Local E(since renamed to burdi) between 2021-22, I and my colleague o-j-bradley implemented an association rule mining algorithm based on an existing research paper. This is a good example of tested and typed python implementation which has been published in pypi and conda. My contributions:

• Understand and implement the algorithm as set by the paper:

  Anthony J.T. Lee, Chun-Sheng Wang, An efficient algorithm for mining frequent inter-transaction patterns, Information Sciences, Volume 177, Issue 17, 2007, Pages 3453-3476, ISSN 0020-0255, https://doi.org/10.1016/j.ins.2007.03.007. (https://www.sciencedirect.com/science/article/pii/S002002550700151X) Keywords: Association rules; Data mining; Inter-transaction patterns
  

• Automated testing using pytest

• Make the code typed using mypy

• Generate package using cookiecutter

• Publish on pypi and conda-forge

This was a personal project to create a web app for training your musical ear for attaining relative pitch. Deployed link: musicear.web.app.

Technology used:

• React
• Redux
• Typescript
• Firebase
• Material-UI

This project was used to enter for an online AI bot competition for a game of Ultimate Tic Tac Toe in codingame.com.

• Monte Carlo Tree Search algorithm has been implemented from scratch in C++
• The bot put me up in Gold League

In this mini project, MNIST was solved using various ML libraries:

• Keras
• Tensorflow
• Pytorch
• NumPy

Of note, a custom neural network layers module chanflow was implemented using NumPy which mimics the call structure of tensorflow.

• Model implementation is mainly based on https://github.com/miyosuda/disentangled_vae.
• data_generator contains custom generator which generated the 2d and 3d sprites used for training and testing the β-VAE model.
• Used to write the UCL MSc Machine Learning dissertation paper Comparing the VAE and the Beta-VAE under various latent bottleneck dimensionalities and data complexities. See the paper here.