In a nutshell, Machine Learning is a way to give computers the powers of generalization. And that's what I try to do: put [seemingly complicated] things simply. My posts on Machine Learning (ML) focus mainly on recent cutting edge research, and how to make it accessible to everyone. I felt like many academic guides weren't accessible enough, so I strove to make my guides as clear and practically-focused as possible.
Unsure where to start? Here are some of my best / most popular posts:
Revisiting a resurging NeurIPS 2015 paper (and 25 best practices more relevant than that for 2020)Read
Building a classifier to tell you whether your LIDAR patent will be approvedRead
Practical considerations of scaling and implementing ML in the real worldRead
A deep dive into the important 'deep' learning conceptsRead