Compare Losses and Explain LoRA
Company: Netflix
Role: Machine Learning Engineer
Category: Machine Learning
Difficulty: medium
Interview Round: Technical Screen
Answer the following machine learning fundamentals questions:
1. Compare mean squared error loss and cross-entropy loss. When is each appropriate, and how do their optimization behavior and assumptions differ?
2. Explain Low-Rank Adaptation, commonly known as LoRA. How is it applied when fine-tuning a large neural network or language model, and why is it parameter-efficient?
Quick Answer: This question evaluates understanding of loss functions (mean squared error vs cross-entropy) and parameter-efficient fine-tuning techniques (Low-Rank Adaptation/LoRA), measuring competency in optimization behavior, probabilistic assumptions, and efficient model adaptation within the Machine Learning domain and bridging conceptual understanding and practical application. It is commonly asked in technical interviews to assess reasoning about appropriate objective selection, optimization dynamics, and parameterization strategies for large neural networks and language models.