Last semester, I learned about Gaussian Processes. They seemed really intriguing at the first glance, and it turned out they are even more intriguing as you dig deeper. This post is an application-oriented intro to Gaussian Processes. I’ll cover GP regressions, forecasting for time series and usage of GPs in bayesian optimization among other things.
An interactive introduction to wavelets and discrete wavelet transformation for data scientists
I built a practical intro guide to wavelets and discrete wavelet transformation for data scientists. Welcome to magic!
Interpretation of log transformations in linear models: just how accurate is it?
Log-transformations and their interpretation as percentage impact is taught in every introductory regression class. But are most people aware that there is a hidden approximation behind the percentage-based intuition? One that may not be appropriate in some cases?