Approximate inference via variational autoencodersA modern classic connecting probabilistic inference and deep learning.
Dimensionality reduction via principal component analysisWe cover another 'classical' technique, this time for unsupervised learning.
Bias-variance trade-off and the interpolating regimeReproducing a nice result from a recent paper.
Sampling b*sicsWe on a roll cos ya basic.
Revisiting the bias-variance decompositionAnother machine-learning classic before we move onto more advanced topics.
Back to basics with PandasA simple end-to-end example using the scientific python stack.
Mastering the basics is very underratedRebooting the blog with some spicy opinions.
Bay Area II: CFAR WorkshopApparently the most memorable things I learnt at CFAR were the games.
Simplicity is complicated; contraints bring freedomRuminations on Pike, Strunk, and White.
A response to 'The AI Cargo Cult'A short rebuttal to a recent essay.
AIXIjsA web demo for general reinforcement learning.
Bay Area I: San Francisco, Berkeley, & Silicon ValleyA short travel post documenting the first half of my Bay area trip.
Marginalization with EinsteinIn 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 :)
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