1. Machine Learning Case Study: given a prompt, talk through how to formulate a ML problem, staring from what data to use, how to clean those data, preprocessing, ML modeling, validation and evaluation 2. General ML question in details: mathematical derivatives, algorithms comparisons, what methods to use for XXX data or problem, etc 3. Behavior questions: why chase, what to do if meet some stakeholders that does not understand ML , etc.
Derive variance of geometric distribution
Pretty difficult and tough questions, even hard to understand what the interviewer was asking.
Adobe is implementing AI features as an add on to one of its core products. How do you evaluate the proper PnP for the new product suite?
Why are you interested in this position?
Describe your workflow process for blend shapes
They asked me about my dissertation
What do you learn from your failure?
What do you need if some one want a finance from our company?
Parameters to be considered before providing Auto loan? Model feature selection? Logistic Regression?
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