Core machine learning principles, how to apply them, and how to diagnose when they are performing sub-optimally.

The infrastructure, systems and algorithmic concepts necessary to scale your machine learning ideas.

Building an intuition for ML problems by connecting the dots between published experiments.

Observations about the industry, where it’s headed, and what is required to succeed in the changing landscape.