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Applied Scientist Interview Questions
1,174 applied scientist interview questions shared by candidates
How will you build a credit card fraud model
Explain Backpropagation and it's advantages.
Implement a class that implements K means
Explain what the following code segment does, and when someone would use this segment in a real application (included a lengthy discussion about performance metrics)
Questions about AB Testing and churn predictions.
- Longest substring in a string - What is knowledge distillation ? - Large language models - Multi-task models
🔹 1. Conceptual Questions (Beginner–Intermediate) ❓ Supervised vs. Unsupervised learning What is the difference between supervised and unsupervised learning? Give examples of real-world problems for each. ❓ Model Understanding What is overfitting and underfitting? How do you prevent overfitting? What is the bias-variance trade-off? What are precision, recall, F1-score, and when do you prefer one over another? ❓ Algorithms How does a decision tree work? What is the difference between logistic regression and linear regression? How does K-nearest neighbors (KNN) work? What is regularization (L1 vs. L2)? 🔹 2. Intermediate to Advanced Topics ❓ Ensemble Methods How does random forest work? What is gradient boosting (e.g., XGBoost, LightGBM)? Difference between bagging and boosting? ❓ Neural Networks What is backpropagation? What are activation functions and why are they important? Difference between CNNs and RNNs. What is dropout, and why is it used? ❓ Optimization What are common optimizers in deep learning? How does stochastic gradient descent (SGD) differ from batch gradient descent?
Given that we have a machine learning model that performs well locally but when deployed to users doesn't what could possible be the cause of this
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