Explain BatchNorm, optimizers, and L1/L2

Company: Snapchat

Role: Machine Learning Engineer

Category: Machine Learning

Difficulty: medium

Interview Round: Onsite

Quick Answer: This question evaluates a candidate's understanding of core machine learning fundamentals—Batch Normalization, optimizer behaviors (SGD, Momentum, RMSProp, Adam), and regularization methods (L1 vs L2)—and the candidate's ability to reason about training dynamics, parameter effects, and inference versus training distinctions in the Machine Learning domain. It is commonly asked in technical interviews because these topics reveal comprehension of optimization dynamics, generalization and sparsity trade-offs, and implementation implications, testing both conceptual understanding and practical application.