Loading…
Mean Squared Error and Its Gradient is a easy coding interview question covering machine learning, math — the kind asked at FAANG (Facebook/Meta, Apple, Amazon, Netflix, Google) and AI-first companies like Anthropic, OpenAI, and Nvidia.
Implement mean squared error for a batch of predictions, and the gradient of that loss with respect to each prediction. Given y_true = [3.0,-0.5,2.0,7.0] and y_pred = [2.5,0.0,2.0,8.0], the loss is 0.375 and the gradient is [-0.25,0.25,0.0,0.5].
Solution walkthrough on Pro
Upgrade to Pro to unlock the guided hints, the step-by-step walkthrough, solutions in Python, C++, Java, and Go, and complexity analysis.
What's inside this guide
Fully unlocked, no account needed — solutions, walkthroughs and follow-up answers included.
Or start the Study Plan — a week-by-week order for your loop, and week 1 is entirely free.
A full interview loop is more than one round. These are the others, each with what is on the site for it.