The chronicles and technical musings of my journey beyond conscious incompetence
In my previous post , I documented how I built a sophisticated absence generation system that transformed presence-only GPS collar data into balanced training datasets. The system successfully generated over 400,000 samples across multiple datasets using different strategies and parallel processing. But running the complete workflow—from raw GPS data to training-ready features—required executing…
In my previous post , I documented how I transformed raw GPS telemetry data from three elk tracking studies into structured training datasets. I ended with 4,650 points from South Bighorn, 94,591 from Southern GYE, and 104,913 from National Elk Refuge—all representing locations where elk were actually present. But for a binary classification model, presence data alone isn’t enough. I needed…
How I transformed raw GPS telemetry data into a machine learning-ready training set for a general-purpose elk location prediction model—and how I’ll validate it on my upcoming hunt The Problem When I started building PathWild , an AI-powered platform for predicting wildlife locations, I had a clear goal: create a general-purpose model that could predict elk locations across Wyoming based on…
This is the moment where theory meets reality. In the last post, I introduced PathWild and the framework I m following from Emmanuel Ameisen s Building Machine Learning Powered Applications. Now it s time to get our hands dirty with the first major step in Part 1: building heuristics based on domain knowledge. Here s the thing most AI/ML [ ]
I m building PathWild.ai—an AI-powered platform for predicting wildlife activity patterns. But this isn t just about the destination. This series will document everything I learn along the way, forcing me to understand AI/ML concepts deeply enough to explain them clearly. If you re looking to build your own AI/ML project as a beginner, I hope this journey [ ]