a loss function is not a detail of training — it is the definition of the problem. choose squared error and you have asked for the conditional mean; choose absolute error and you have asked for the median; choose hinge and you have asked only for the decision boundary; choose cross-entropy and you have asked for the whole probability. 𐃏 this page catalogues the standard losses, proves what each one’s minimiser actually is, and draws the classic picture that unifies the classification zoo: every one of them is a bribe paid to make the 0–1 loss differentiable.
Agentic
an api is a promise you cannot take back: the moment a second team depends on it, every quirk you shipped is load-bearing. 𐃏 this page is the checklist for making promises deliberately — resources and verbs, status codes, pagination, versioning, errors, auth, and rate limiting, with the http machinery treated as the application-layer protocol it is (Kurose, James F. and Ross, Keith W., 2020).
approximate inference in graphical models — forward and rejection sampling, likelihood weighting, gibbs sampling and mcmc, and loopy belief propagation, with the trade-offs that decide which to reach for.
an autoencoder is a network trained to do the one thing that sounds useless: output its own input. the trick is the obstacle course in the middle — a bottleneck, a corruption, a penalty — that makes verbatim copying impossible, so the network is forced to learn what about the input is worth keeping. 𐃏 the family tree below runs from the linear special case (which is pca wearing a trenchcoat) to the variational autoencoder, which turns the whole construction into a generative model (Goodfellow, Ian, 2016).
bananagrams hands you a fistful of letter tiles and one instruction: arrange all of them into a connected crossword before anyone else does.
𐃏
this page documents the real solver living in this repo at static/code/bananagrams/ — a haskell heuristic search in haskell-imp/ plus a playable js incarnation served at /code/bananagrams/ on this site — rather than a from-scratch design; a compact python reference solver (trie + backtracking) is developed at the end to make the algorithmic skeleton explicit.
bayesian networks from first principles — the chain rule, conditional probability tables, d-separation and its valve intuition, markov blankets, i-maps, naive bayes classifiers and gaussian networks.
✦ The bazaar is where the things I build get sold: one-time purchases, delivered the moment payment clears, no accounts.
Four stalls:
- reMarkable — native plugins for the Paper Pro → bazaar.abaj.ai/remarkable
- Flashcards — hand-authored Anki decks → bazaar.abaj.ai/flashcards
- Notes — typeset study bundles → bazaar.abaj.ai/notes
- Prints — art from the studio of 佐野貴代美 → bazaar.abaj.ai/prints
Each stall’s page carries a heading per listed item — a small brief of what it is and why it’s worth money.
Hand-authored Anki decks, exported clean and sold at bazaar.abaj.ai/flashcards. The rule for what gets sold: only decks that are my own words — anything derived from textbooks, publisher audio, or downloaded shared decks stays out of the shop (sourcing rules in the bazaar repo’s SPEC §7). All four are in pre-sale cleanup: verbatim third-party fragments get stripped before any deck ships.
CS Interview Prep
~1,300 cards covering the software-interview surface: Java, Python, data structures & algorithms, OOP, git, and interview framing. One idea per card, short prompts. Coming soon → /p/cs-interview-prep
Typeset study bundles — LaTeX-set PDFs of my own summaries — sold at bazaar.abaj.ai/notes. The gate for this stall is the strictest in the bazaar: only 100%-own-authorship summaries, no lecturer material, course branding stripped. Free counterparts live across the site’s typeset-PDF wiki pages; the paid bundles are the ones polished into standalone books.
STAT 571 — Probability & Measure
My course notes from the University of Alberta’s graduate probability-and-measure course: a ~50-page arc from sigma-fields to the ergodic theorem — Carathéodory extension, the convergence theorems, Fubini–Tonelli, L^p, Borel–Cantelli, LLN, CLT — thirteen lectures compressed to revision speed. I write my own notes for every course I take; these ship with the custom LaTeX preamble (Baskervaldx + newtxmath, the theorem environments) so yours can look this good too. Live → /p/measure-theory-notes
Art prints from the studio of 佐野貴代美, sold at bazaar.abaj.ai/prints.
The stall is being stocked — first pieces are in preparation. Each print will get its own heading here as it’s listed.