Details on Lemmatization and Stemming
Learning Engineer Interview Questions
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1. Explain the bias-variance tradeoff 2. What is k-fold cross validation and why is it important? 3. How would you deal with class imbalance?
Deep learning questions.
Der Loss konvergiert nicht, wie würden Sie dieses Problem beheben?
Neural network types, differentiation, basic statistics, MLE, discriminative vs generative models, graphical models
1. HR interview 2. 1st round interview: 30 mins, basic ML question 3. 2st round onsite: 5-6 hrs take home project, 2hr presentation. The hardest part would be onsite, many challenging questions. You have to find the best solution for the company. take home project is time-comsuming
What are some regression metrics you are familiar with?
Tell me about yourself and why should we hire you.
¿Que es una metodología agile? ¿Que es ML supervisado y no supervisado?
No recuerdo las preguntas exactas, pero se enfocaron en diseño de pipelines, comunicación técnica según perfil, y desafíos comunes en sistemas de machine learning en producción.
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