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This is a medium difficulty Machine Learning question, commonly asked during Onsite rounds at Databricks.

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This question is commonly asked for Machine Learning Engineer candidates at Databricks during technical interviews.

Implement Gradient Descent Regression | Databricks Interview Question

Implement Gradient Descent Regression

Last updated: Apr 22, 2026

Quick Overview

This question evaluates understanding of linear regression and gradient-based optimization, including loss formulation, gradient derivation, parameter updates, hyperparameter tuning, and debugging of convergence issues.

Databricks

Feb 7, 2026, 12:00 AM

Machine Learning Engineer

Onsite

Machine Learning

Implement linear regression from scratch to predict a continuous target y from input features X using gradient descent. Use mean squared error as the training objective.

Be prepared to:

write the training loop,
derive or explain the gradient updates,
choose and tune the learning rate,
explain early stopping,
and debug common reasons why the model does not converge.

Solution

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