Machine learning knowledge and code. - LeetCode (medium, hard with Python) - AUC definition - Methods for dealing with long-tailed data - Causal inference - Machine learning questions - Deep learning methods such as attention mechanisms
The interview started with discussions on project experiences, followed by questions on deep learning architectures (diffusion models, recommendation systems, etc.). Two mid-level LeetCode questions were then asked, covering: * Binary trees * Dynam
The interview began with a medium-difficulty coding challenge. Then came background questions, asking about projects on the resume. The interviewer also inquired about experience with big data systems, model deployment, and optimization.
Machine learning knowledge and code. - LeetCode (medium, hard with Python) - AUC definition - Methods for dealing with long-tailed data - Causal inference - Machine learning questions - Deep learning methods such as attention mechanisms
The interview started with discussions on project experiences, followed by questions on deep learning architectures (diffusion models, recommendation systems, etc.). Two mid-level LeetCode questions were then asked, covering: * Binary trees * Dynam
The interview began with a medium-difficulty coding challenge. Then came background questions, asking about projects on the resume. The interviewer also inquired about experience with big data systems, model deployment, and optimization.