The BytePlus platform has n servers, where the data stored on the ith server is represented by the array memory. To manage the server efficiently you can perform the following operation any (possibly zero) number of times: • Choose an index idx, such that 1 ≤ idx s n/2. • Swap the data of the pair of servers that are equidistant from the beginning and the ending of the array memory and have a distance less than or equal to idx. The total working efficiency of the servers is calculated as the sum of the product of the data present in each server with the index of that server. Given an integer n, and an array emory, find the maximum possible total working efficiency that we can get, since the total working efficiency can be very large print it modulo 109+7.
Machine Learning Engineer Interview Questions
Machine Learning Engineer Interview Questions
Les entreprises s’appuient sur les machine learning engineers pour les aider à concevoir et à améliorer les systèmes qui permettent à leurs logiciels de s’améliorer eux-mêmes, plutôt que d’être programmés. Au cours de l’entretien, préparez-vous à être longuement interrogé sur vos connaissances en informatique et en science des données et, en particulier, sur votre capacité à reconnaître des modèles et des tendances. Un diplôme en informatique ou dans un domaine équivalent sera exigé.
Questions d'entretien d'embauche fréquentes pour un machine learning engineer (H/F) et comment y répondre
Question 1 : Quels sont les algorithmes, termes de programmation et théories les plus importants à maîtriser en tant que machine learning engineer ?
Question 2 : Comment expliquer l’apprentissage automatique à quelqu’un qui ne comprend pas ce domaine ?
Question 3 : Comment se tenir informé des dernières nouveautés et tendances en matière d’apprentissage automatique ?
8,202 machine learning engineer interview questions shared by candidates
1.Situation based questions about Precision, Recall, Confusion Matrix 2. Python code reviewing
- Basics of python and interview questions related to python - problem solving - ML Scenario based question., etc
Briefly describe what the problem was in your projects listed in the resume and your approach
do you know auto encoders
Describe a data processing pipeline you developed.
How did you collect data?
Multiple choice questions and a programming question. Questions on L1 and L2 regularization, precision and recall, feature scaling, etc.
Basics of machine learning
What is your experience with Python?
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