Data Science

Data science is like the middleground between Machine Learning and Data Engineering. I thought I wasn’t interested in this as much, but I did some work at Ericsson and learned a lot more about this field again. Visualizing data is sometimes interesting.

In Reinforcement Learning

To compare different algorithms, you can conduct a

  • Parameter Study
  • % Optimal Action selected vs. Iterations
  • Average Reward vs. Iterations


My Computer Vision Stack

Reflection for Musashi AI Challenge 2022

  • Problem definition is VERY important. We over-labelled and did not configure our IoU properly. We didn’t want to under predict, but if we had more time, we should really look more carefully at the predictions
  • Become really good at Docker, super useful
  • Get better practicing doing Ensemble Modelling. If you get your model working earlier, you can make better predictions, and so for your next one you will be better prepared
  • Hand labelling a small sample and doing Transfer Learning is SOO powerful with Transfer Learning is SOO powerful with Roboflow,

Example of data science job description