News & insights
Articles on ML research literacy, systems, data, and teaching—same card layout as our course catalog.
A practical order of operations: abstract, figures, method, then experiments—so you know what to skim and what to study.
Latency, batching, KV-cache memory, and evaluation—not just swapping in a bigger model.
If retrieval misses the right chunk, no prompt tweak will save the answer. Split your eval into recall, grounding, and fluency.
Go deeper on optimization, evaluation, and data—without replaying every undergraduate lecture.
Guidelines, adjudication, and slice-aware QA beat raw throughput when labels drive production decisions.
Concepts stick when you connect equations to experiments and papers to the code you can run.