by Dr. Owns | Apr 10, 2025 | Analytics, Artificial Intelligence, Data and Information, Decision Support
Ground truth is never perfect. From scientific measurements to human annotations used to train deep learning models, ground truth always has some amount of errors. ImageNet, arguably the most well-curated image dataset has 0.3% errors in human annotations. Then, how...
by Dr. Owns | Apr 10, 2025 | Analytics, Artificial Intelligence, Data and Information, Decision Support
Did you ever spend months on a Machine Learning project, only to discover you never defined the “correct” problem at the start? If so, or even if not, and you are only starting with the data science or AI field, welcome to my first Ivory Tower Note, where I will...
by Dr. Owns | Apr 9, 2025 | Analytics, Artificial Intelligence, Data and Information, Decision Support
Introduction I’ve always been fascinated by debates—the strategic framing, the sharp retorts, and the carefully timed comebacks. Debates aren’t just entertaining; they’re structured battles of ideas, driven by logic and evidence. Recently, I started wondering: could...
by Dr. Owns | Apr 9, 2025 | Analytics, Artificial Intelligence, Data and Information, Decision Support
Gradient boosting is a cornerstone technique for modeling tabular data due to its speed and simplicity. It delivers great results without any fuss. When you look around you’ll see multiple options like LightGBM, XGBoost, etc. Catboost is one such variant. In this...
by Dr. Owns | Apr 9, 2025 | Analytics, Artificial Intelligence, Data and Information, Decision Support
I used to avoid time series analysis. Every time I took an online course, I’d see a module titled “Time Series Analysis” with subtopics like Fourier Transforms, autocorrelation functions and other intimidating terms. I don’t know why, but I always found a reason to...
by Dr. Owns | Apr 9, 2025 | Analytics, Artificial Intelligence, Data and Information, Decision Support
Working with products, we might face a need to introduce some “rules”. Let me explain what I mean by “rules” in practical examples: Imagine that we’re seeing a massive wave of fraud in our product, and we want to restrict onboarding for a particular segment of...
