Machine Learning¶

I find theoretical math and machine learning to be pretty fun. I think that good practical techniques that work well are derived from a theoretical root.

Twitch on Theory In Convex Optimization Deriving everything we want in optimization from Taylor Theory and with small modification on some assumptions or the way we design things, we get completely different families of algorithms.
All You Need Is Constraint Solving All hard problems that we want to solve can be framed as a constraint solving process if we look at them from a particular perspective. Both in math and in life.
From Set We Create All A very simplified and naive attempt to discuss about how mathematics are built up from sets using point set topology, that the usual math in R^n is just a small example of the realm of mathematics.
Unfolding Stochasticity Sequentially Modeling interactions between stochasticity across time sequentially through the key representational example of Random Walk.
Lend It Some Confidence There are deep connections between statistics and probability, even on very basic statistics levels.
From Random Walks to Generating Molecules From random walks to graph transformers to hierarchical graph generation, tracing how the field learned to represent and generate graph-structured data.