Snarky(?) pure math explanation of supervised machine learning:
Grid Search no more!
Here is a very nice illustration from Bergstra & Bengio (2012) why Random Search is often superior to Grid Search for purposes of parameter choice -- Random Search gives by far the better approximations to the important univariate parameter distributions.
Ah! No that was the book of the student of the scholar I was thinking of (which was also very good mind you!)
The book I was thinking of is called 'The Metaphysics of Experience: A Companion to Whitehead's Process and Reality' by Elizabeth M. Kraus. It's meant to be read alongside P&R and help make sense of it
Well most of the cool work on Whiteheads philosophy comes out of Process and Reality, but it's notoriously dense and difficult to understand. I like to say it took me the better part of 6 years to finish it (of course that's an exaggeration, but from my first starting to read it in undergrad to finishing it in grad school it was close)
Modes of thought is much more accessible, but kinda suffers from being 'fluffy', especially in the middle - 1/2
Also I'm really new to Mastadon and am definitely kinda confused. Any advice is appreciated!
Hello! I see everyone doing one, so here's my #introduction
I'm a Data Analyst working at a smallish health center. I have a masters in #publichealth and another in #philosophy . Most of my work in philosophy was on process philosophy & A.N. Whitehead, and most of my work in public health is on #dataanalysis and #substanceabuse , specifically #opioid use.
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