Explain regularisation procedures in deep neural nets
Applied Scientist Interview Questions
1,182 applied scientist interview questions shared by candidates
1. Leadership principle questions (around 2) with follow-up questions. 2. Basic ML questions. 3. ML use case problem with follow-up questions.
- How to handle difficult situations - How to handle different opinions between colleagues - How a CNN works - How a RNN works - Did you work with Transformers? What is Attention? - Summary metrics for NLP - CODING: Two sum but with multiplication actually - How a BiLSTM works - Metrics for regression BONUS INTERVIEW WITH OTHER TEAM (more friendly) -Bagging and boosting -Forecasting example -Computational difference between XGBoost and Random Forest
Explain gradient descent, batch norm, how to accelerate, how to parallel, how to change the batch size when parallel and how to save memory for running.
Q: Questions related to my published paper.
Why Amazon? Mention one challenge.
Science depth Science breath Leadership principals Coding
What is variance and bias tradeoff
Star methods for Leadership principles, use examples.
what are the differences between cross-entropy loss and contrastive loss?
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