Explain Overfitting and Transformer Basics
Company: J.P. Morgan
Role: Data Scientist
Category: Machine Learning
Difficulty: medium
Interview Round: Technical Screen
Overview: This question evaluates proficiency in core machine learning competencies such as overfitting and generalization, selection and regularization of loss functions for classification and regression, encoder-decoder sequence architectures, and self-attention mechanisms including queries, keys, and values, as well as considerations like bias–variance tradeoffs, masking, and attention computational cost. It is commonly asked in technical interviews for Machine Learning and Data Scientist roles because it probes both conceptual understanding and practical application of training dynamics, model architecture choices, and scalability trade-offs within the Machine Learning domain.