Hello! I am a programmer from Latvia, currently I am making a transition to machine learning/artificial intelligence, taking online courses, reading books, building personal projects and doing anything that gets me closer to this goal.
- Master's degree in IT, University of Liepaja, graduated in 2018.
- Udacity Deep Learning Nanodegree - graduated in 2020
- Azure Cloud Scholarship on Udacity - currently enrolled.
Developing NN models for X-ray analysis, working on NIH and CheXpert datasets.
Out of 14000 applicants 5000 were picked for stage 1. For stage 2, 300 were picked. I managed to be among them.
Short course description:
What’s the earliest we can predict cancer survival rates, and what schools do the best job of educating children? You can only answer these questions with very rare access to private and personal data, but access to this personal data requires that you master methods for the principled protection of user privacy. While not all privacy use cases have been solved, the last few years have seen great strides in privacy-preserving technologies.
This course introduces three cutting-edge technologies for privacy-preserving AI: Federated Learning, Differential Privacy, and Encrypted Computation. It teaches how to use the newest privacy-preserving technologies, such as OpenMined's PySyft. PySyft extends Deep Learning tools such as PyTorch—with the cryptographic and distributed technologies necessary to safely and securely train AI models on distributed private data.
Team projects I worked on:
Disease detection using Chest X-rays.
Smog Detection was ranked #2 out of 141 showcase projects.
I also won the Slack App challenge with my app AMAdillo - it is a Q&A app that allows looks up similar questions in a database, based on fuzzy string matching.