Some assumptions are just plain weird.
VeryHighAccuracy[tm] as a Problem.
The unknowns might be more interesting.
We should evolve beyond it.
GridSearch is not enough: Part Three.
An Ode to Pipes, Seeds and Simplicity
GridSearch is not Enough: Part Two.
Thoughts on automating automation itself.
Algorithms can detect outliers, but how do you select algorithms?
Predictions without Foresight can be Anticipated to become Dreadful.
An Exercise in Systemic Counting
GridSearch is not Enough: Part One.
And Something Obvious about Albert Heijn.
And it's not just because that's whats *actually* being predicted.
A small point to point out a difference.
The only servers in my stack are the sensors themselves.
Pretending there's an optimal way to drink it.
Feel free to be a bit weary.
A probability problem involving 40,000 sent letters.
Sort of came up with an alternative to VI here.
The more CPU's you add, the worse it gets.
Combine the Neural with the Normal.
A VeryGood[tm] name for a VeryGood[tm] Algorithm.
How to search in search space.
A plausible (and general) method for model selection.
A convenient overlap.
Join me in preventing this.
Stick a Network in the Q-learning algorithm.
Never let your school get into the way of your regression.
A simple introduction to PyMC3.
Even more of a Sequel than SQL.
An investment opportunity and sampling.
It's different than Python, but S3 isn't *that* bad.
Scribbles as an Algorithm Service.
An Attempt at an OptimalPortfolio[tm]/
XOR turns out to be a bad argument.
Merely *a* argument, but one that I like.
Some observations but also downsides.
How I got an A+ for measuring the wrong thing.