Apr 13 · 14 min read · You've trained a machine learning model and want to tune its hyperparameters. Each evaluation takes hours. You've tested 6 configurations so far. Where should you try next? If you read our hyperparame
Join discussion
Apr 1 · 12 min read · Imagine you're a politician touring a chain of islands. Each island has a different population, and you want to spend time on each island in proportion to its population — more time on crowded islands
Join discussion
Mar 29 · 16 min read · In the MLE tutorial, we estimated a coin's bias by finding the single parameter value that maximises the likelihood. Flip a coin 3 times, get 3 heads, and MLE says \(\hat{\theta} = 1.0\) — the coin al
Join discussion
Mar 27 · 11 min read · Imagine you're a casino inspector. You suspect a dealer has been switching between two biased coins, but you only have records of the outcomes - not which coin was used for each game. How do you figur
Join discussion
Mar 26 · 15 min read · You've collected data and you have a model in mind — maybe a Gaussian, maybe a coin flip. But the model has parameters, and you need to find the values that best explain what you observed. How? Maximu
Join discussion
