Posts
-
Approximate inference via variational autoencoders
A modern classic connecting probabilistic inference and deep learning. -
Dimensionality reduction via principal component analysis
We cover another 'classical' technique, this time for unsupervised learning. -
Bias-variance trade-off and the interpolating regime
Reproducing a nice result from a recent paper. -
Sampling b*sics
We on a roll cos ya basic. -
Revisiting the bias-variance decomposition
Another machine-learning classic before we move onto more advanced topics. -
Back to basics with Pandas
A simple end-to-end example using the scientific python stack. -
Mastering the basics is very underrated
Rebooting the blog with some spicy opinions. -
Bay Area II: CFAR Workshop
Apparently the most memorable things I learnt at CFAR were the games. -
Simplicity is complicated; contraints bring freedom
Ruminations on Pike, Strunk, and White. -
A response to 'The AI Cargo Cult'
A short rebuttal to a recent essay. -
AIXIjs
A web demo for general reinforcement learning. -
Bay Area I: San Francisco, Berkeley, & Silicon Valley
A short travel post documenting the first half of my Bay area trip. -
Marginalization with Einstein
In this post we explore a convenient trick for marginalizing discrete distributions in directed acyclic graphs using NumPy's Einstein summation API. -
Linear regression & Hello World!
A brief look at some cool results that are often overlooked in short treatments of linear regression. Also, my first blog post! Yay :)
subscribe via RSS