Questions d'entretiens - Data engineer

1. How do you use NN to reduce dimensionality? Approximate Nearest Neighbors using random projections is a dimensionality reduction technique. 2. Can you model time series as a linear regression model? Yes, using an autoregressive modeling technique. 3. a) Can you use resampling methods like bagging to estimate the max of a population? b) Why is bagging a variance reduction scheme? Bagging reduces variance b/c it trains many deep trees which when averaged together lowers variance while bias stays constant. 4. Why is the use of minibatch to minimize a function computationally more efficient than any other methods? Mini batch is a nice middle ground between an accurate gradient (batch GD) and memory constraints (SGD). Mini batches allow you to exploit data parallelism and also accurate gradient updates. 5.Gambler's ruin problem. Don't know. Maybe gamblers fallacy? 6. Assume that in a time series, some data are missing. How do you handle that? A. average out the existing values. Okay, so you want to average out the existing values, but how do you define the the new time series as a single function? A. Use characteristic or indicator function.