Self introduction and my work experience.
Applied Scientist Interview Questions
1,174 applied scientist interview questions shared by candidates
Explain Backpropagation and it's advantages.
Difference between correlation and covariance.
Q: What are the pros/cons of using trust region optimization algorithms instead of temporal difference ones to do planning in RL?
shortest sub array to sort
- Longest substring in a string - What is knowledge distillation ? - Large language models - Multi-task models
AI Coding Interview: Code K-means clustering One interview on ML/NLP Depth One interview on Research/Projects One interview on Behavioral Skills (with HM)
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
alot on resume and theory on concepts covered in resume
🔹 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?
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