Google ML Foundations Interview: Loss Functions Guide

Overview

You will be asked to explain what a loss function is, why it matters, and how it guides optimization during training (e.g., gradient descent). Expect to discuss common choices for regression and classification, show simple derivations, and justify which loss fits a given task.

Core content

A loss function maps predictions and targets to a scalar that the optimizer minimizes. For regression you should know mean squared error (MSE): MSE = 1/n Σ(y_i - ŷ_i)^2. For classification you should know cross-entropy; for the binary case: Cross-Entropy = -1/n Σ[y_i log(ŷ_i) + (1 - y_i) log(1 - ŷ_i)]. Explain properties such as convexity, differentiability, and whether the loss produces well-behaved gradients for your chosen model outputs (raw scores vs. probabilities).

Flow in an interview

You’ll typically be guided through: (1) definition and intuition; (2) mathematical form and gradient derivation; (3) comparison (MSE vs cross-entropy); (4) practical concerns like numerical stability (log-sum-exp), class imbalance, and robust losses; and (5) quick pseudocode or implementation notes.

Skill signals

Interviewers look for calculus (gradient computation), probability (interpreting cross-entropy as likelihood), optimization intuition (vanishing gradients, scale sensitivity), and practical ML engineering (choosing loss for task, handling imbalance, numerical stability). Prepare to propose or analyze custom loss functions and relate training loss to evaluation metrics.