Exploration ·
Follow a query from the box you type it into to the ten blue links: how text becomes terms, how an inverted index stores and compresses them, how BM25 scores a match, how we know a ranking is any good, and how the whole thing is split across thousands of machines — with the tools running live between the paragraphs.
Tool ·
Paste TREC qrels and runs (or CSV) and get P@k, recall@k, MAP, MRR, nDCG@k with linear and exponential gain, ERR and R-precision per query — checked against trec_eval and gdeval — then compare two rankers with paired t, Wilcoxon, randomisation and bootstrap tests, and simulate team-draft interleaving.
Tool ·
The mathematics under machine learning, hands on — least squares with draggable points (residual squares, outliers, L1 vs L2, normal equations vs gradient descent and SGD), logistic regression and its cross-entropy bowl, a computation graph with forward and backward passes against dual numbers and finite differences, TF-IDF and BM25 search, entropy with a Huffman tree builder and KL divergence, and softmax temperature with an attention matrix.