Nestor S.

A Gaussian Mixture (GM) is a popular clustering model that is usually fitted using the Expectation-Maximization (EM) algorithm. This makes the model difficult to scale since EM is a batch algorithm, not suited for very large datasets or data streams. Taking this paper as starting point, in this project I developed a Scala library that implements accelerated stochastic gradient descent GMMs, which solves the problems mentioned above and achieves fast convergence; it can run sequentially or in parallel using Spark.

Python package implementing a GCP-based end-to-end machine learning pipeline for generative deep learning models using typeface data. It uses Beam for data preprocessing, Tensorflow for model training and MLFlow for experiment tracking.