Implement a dice loss and multi class dice loss, implement a graph algorithm, some theoritical ML questions.
Applied Scientist Interview Questions
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ML questions cover topics such as data imbalance, collinearity, feature selection, linear regression, logistic regression, L1/L2 norm regularization; ML application question -- how would you design a recommendation system that recommend books to users;
Write the loss function for the logistic regression. Prove that it has a global minimum.
what is the different non-deterministic and deterministic classifier
Detailed analysis of the resume
Product Sense Question - How do you tag an incoming unknown product with amazon catalogs (such as toys, books, electronics). Provided description and a couple of pics about of the product Programming Question - Iterating through a list of tuples and performing some algorithm on the tuple values (simple one).
PCA and similar methods, signal processing
First round: - phone/video interview with one person. - Asked the typical DS interview questions (overfitting/cross validation), talked about my ML experiences. - Asked a basic coding question: given an animal, print out the noise it makes. Basic things like polymorphism/inheritance, briefly touched on string similarity Second round in person (5 interviews): - Lots of leadership principle/behavioral questions - One coding interview. Given a database of book titles and number of copies sold, how do you identify the top N most-sold books. Basic algorithm/data structures of things like priority queues/heaps, space-time complexity analysis, live-coding. Even if you miss the correct data structure, they provide some hints along the way so you can complete the problem - Multiple DS interviews, from things like typical DS interview questions and your ML experience, to an applied DS question (deduplicating transactions, how would you solve this problem, how would you build/train/score a model, how would you scale it)
How did the last product you worked on help the customer?
Write down the pseudo-code of Kmeans Detailed questions of random forest methods
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