Modelling and inference rather than platform: supervised methods, features and leakage, metric choice, experimentation and statistics, and defending a result to a stakeholder.
Ratios improve when the denominator shrinks and averages improve when the mix shifts, so a rate can rise while every underlying cohort worsens; you catch it by pairing every rate with its absolute counts, decomposing by segment, and defining guardrails before launch.
Say yes if it is true, give a stage and a rough timeline without naming companies, and use it to set a decision date rather than to imply a bidding war. Inventing a process you do not have is the one answer that can end badly.
Find out what the date is being used for, then commit to something you can actually control — a timeboxed discovery end, a date for the date, or a narrow first slice — and never issue a single-point estimate for scope that is still undefined.
Backpropagation applies the chain rule backwards through a network, reusing each layer's cached activations to get every parameter's gradient in one pass. Gradients vanish when many small factors multiply, and it shows as a stalled loss with early-layer weights barely moving while the last layers still train.
Work backwards from the review capacity. Fix the alert budget the team can clear, evaluate precision and recall at exactly that budget on data at the real prevalence, define the label and its arrival delay before modelling, and never report accuracy, which a do-nothing model scores 99.95% on.
Because you cannot score a million-item catalogue per request inside a few tens of milliseconds. Retrieval reduces millions to hundreds with a cheap model judged on recall, then a far more expensive ranker orders those hundreds and is judged on the ordering it produces at the top of the list.