Behavioral questions
The questions asked by the interviewer were relatively simple, roughly as follows:
Give me a story where you have limited time to finish a project
Give me a story where you actively learn a new skill (Learn and Be Curious)
Give me a story (where you showcase "Bias for Action")
Give me a story (where you showcase "Ownership")
I felt that the interviewer seemed to be filling out a form during the interview, where they needed to summarize my story into bullet points. So, thinking in reverse, the script prepared by the interviewee needs to have these bullet points, concise but informative and on-topic (Amazon leadership principles). Helping the interviewer fill out the form equals helping yourself~
ML Basics
The interviewer was very nice, and the questions were quite basic, but I think the specifics depend on the team.
What are GD and SGD, and what are their differences? Why use SGD?
What is batch size?
What are supervised learning and unsupervised learning? Please give some algorithm examples.
Explain the principles of linear regression/logistic regression from scratch.
Explain the loss function of logistic regression.
Explain basic concepts in machine learning: learning rate, regularization.
How to deal with overfitting?
Explain bootstrapping and boosting.
Explain the principles of the boosting algorithm.
What is the relationship between reinforcement learning (RL) and supervised learning and unsupervised learning?
How is reward used to update the policy?
How are neural networks integrated into reinforcement learning?
What are the differences between Transformer and RNN? What bottleneck does it solve?
What is embedding?
In the embedding process, how do you distinguish the semantics of "King" in the context of chess and in the context of monarchy?
How to fine-tune large language models (LLM) with limited computing resources?
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