Interview Guides

Gradient Descent and Local Minima

Medium

model trainingGradient Descentoptimization

Problem

Scenario

You're training a supervised learning model and need to explain how the optimizer updates parameters during training. You also want to reason about what can go wrong when the loss surface is non-convex.

Question

Explain how gradient descent optimization works and how you would prevent a model from getting stuck in local minima.

Problem

Scenario

Question

Explain how gradient descent optimization works and how you would prevent a model from getting stuck in local minima.

Practice Python

Python 3.10

Open on desktop for the full Python editor with syntax highlighting and autocomplete.

Up next

Linear Regression and Gradient DescentMedium Gradient Descent BasicsEasy Stochastic Gradient Descent BasicsEasy

Next question

All questions/Machine Learning

Gradient Descent and Local Minima

Medium

model trainingGradient Descentoptimization

Problem

Scenario

Question

Explain how gradient descent optimization works and how you would prevent a model from getting stuck in local minima.

Problem

Scenario

Question

Explain how gradient descent optimization works and how you would prevent a model from getting stuck in local minima.

Practice Python

Python 3.10

Open on desktop for the full Python editor with syntax highlighting and autocomplete.

Up next

Linear Regression and Gradient DescentMedium Gradient Descent BasicsEasy Stochastic Gradient Descent BasicsEasy

Next question

Gradient Descent and Local Minima | Dataford Interview Questions