After spending a decade-long career semi-arbitrarily picking up and learning things — almost, but not quite, a random walk — I like to imagine I can do a little bit better over the next ten years. Writing it out as an experimental newsletter is a way to track progress, maintain a little bit of discipline and a way to stay on brand.
Photo by Marjan Blan | @marjanblan on Unsplash
With experience I’ve realized that
one lifetime is nowhere near enough to learn all that I would like to
I learn by building; watching videos or taking notes doesn’t stick
everything flows much more smoothly if I’m having fun
Of course, it’s also somewhat valuable to have a reason for learning
going deep on-demand as I work on a project: this helps set realistic scopes
going wide and staying current, just to build an index of what’s possible
satisfying my curiosity, and simply following anything interesting
With the core framework laid out; taking stock of where I’m at today:
I’m building debugging related tools for ml engineers tackling large problems
with a somewhat mechanical understanding of how models are trained
and a somewhat deeper understanding of software / systems
somewhat topically, anything that would help me model Covid
… and to wrap things up, I’ve been fascinated by Monte Carlo methods recently
Where I’d like to be
understand ML models end to end: Math, Software & Hardware
up to date with the current state of the industry — with habits of reading papers
and the way to confirm that I’ve learned any of this is through tangible projects:
explained derivations, exercises for Math;
working projects for Software & Hardware;
with a lot of introspection and tools around the space.