I'm a mathematician who's aspiring to be a Machine Learning Engineer (MLE).
I did a PhD in Maths (Algebraic Geometry) and decided that a career in academia wasn't for me, and that I'd rather make use of my mathematical and statistical knowledge to build things that make me money by making even more money for other people.
I am of Ghanaian, Sierra Leonean and British heritage, and beyond my personal professional goals, I would like to somehow apply my budding skills to contribute in some small way to the scientific, technological, and economic advancement of the African continent and its people.
With that being said, I think that above all other technologies, machine learning models have the ability to turn our world into a dystopian hellhole depending on how they are used, and I think that MLEs should be cognisant of that when selecting projects and jobs.
I'm determined to complete end-to-end ML projects of various kinds to grow my skills.
- 🔭 I’m currently working on:
- a Streamlit web application that predicts user demand for the City of Chicago's Divvy bike-sharing service.
- creating and deploying an exchange rate prediction service as a REST API. The purpose of this project is to force me to become familiar with FastAPI.
- creating a service that receives a picture of a mushroom, and identifies its genus, and eventually its species. I'm using this project to force myself to transition to Pytorch and delve deeper into deep learning (I had used Tensorflow before, but Pytorch appeals to me a great deal more). I have an eye on learning some of the more "advanced" network types and architectures as my skills (and my courage) grow.
- Become more proficient with Pytorch. I initially learned to build some networks with Keras in Tensorflow, but I've decided to switch libraries.
- Learn about some of the more advanced neural network architectures, such as GANs and transformers, and use them to make cool stuff.
- Learn about model deployment on AWS and GCP.
- Start reading deep learning papers.
👯 I’m open to collaborating with others on virtually any project, as I'm eager to learn about as many machine learning architectures and tools as possible.
