Netflix数据相关面经_Netflix数据相关面试真题及高频题

1.Data Processing with Numpy

2.Calculate Distributions' Divergence

3.Overfitting and Underfitting

4.Derivation of Basic Loss Functions

5.Probability Calculation

6.Differences between Recall and Precision

7.ML Backend Knowledge

8.Implementing TFIDF

9.Project Deep Dive

10.ML Backend Design

11.Design a Data Model and Schema for an Ads Booking, Delivery, and Report System

12.Perform Standard Normalization

13.Handwrite a Decision Tree Generation Process

14.Probability of Blue Wrapper Candy

15.Movie Collection Scoring Function

16.Coding Exercise

17.In-depth Machine Learning Discussion

18.General Machine Learning Knowledge

19.Panel Exercise Presentation

20.Why is penalization necessary and how is it implemented in neural networks?

21.What is a ROC curve, and can you explain sensitivity and specificity?

22.Discuss your favorite machine learning model.

23.Explain the difference between supervised and unsupervised learning.

24.Handling Multiple Testing at Netflix

25.Designing an A/B Test for Netflix

26.Balancing Covariates in Research

27.Estimating Spill-over Effect in Research

28.Handling Specific Video Processing Tasks

29.Panel Interview and Hiring Manager Questions

30.Advanced Machine Learning Concepts

31.Basic Machine Learning Concepts

32.Machine Learning Fundamentals

33.Classifier Metric Selection for Stock Performance Detection

34.Gradient Calculation

35.Use of Dropout in Neural Networks

36.Clustering standard error

37.Conducting a placebo test

38.Using diff-in-diff to estimate a national level policy shock

39.Explain the concept of diff-in-diff

40.Details of Propensity Score in Observational Causal Inference

41.Metrics and Sample Size Determination for A/B Testing to Improve Sign Up Rate

1. Data Processing with Numpy

Explain how to process input data using numpy, highlighting the differences from typical leetcode-style problems.

2. Calculate Distributions' Divergence

Given two distributions, describe how to calculate their divergence using numpy.

3. Overfitting and Underfitting

Discuss the concepts of Overfitting and Underfitting in Machine Learning.

4. Derivation of Basic Loss Functions

Explain the process of deriving basic loss functions in Machine Learning.

5. Probability Calculation

Describe how to calculate probabilities in a given scenario.