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:
A Guide to starting from scratch, based on my own experiences (WARNING: LONG READ)Read
Demo of Few-Shot Unsupervised Image-to-Image TranslationRead
An increasingly necessary component of being engaged on ML TwitterRead
An example that's probably closer to what you're experiencing in coding interviewsRead
A simple way to use TPUs with minimal hardware optimizationRead
Announcement for the work I did with the TFP Team at GoogleRead