Practice Variants Of Gd (2.3.2) - Optimization Methods - Advance Machine Learning
Students

Academic Programs

AI-powered learning for grades 8-12, aligned with major curricula

Engineering Courses
Professional

Professional Courses

Industry-relevant training in Business, Technology, and Design

Certifications
Games

Interactive Games

Fun games to boost memory, math, typing, and English skills

Variants of GD

Practice - Variants of GD

Learning

Practice Questions

Test your understanding with targeted questions

Question 1 Easy

What is Batch Gradient Descent?

💡 Hint: Think of its stability and computational accuracy.

Question 2 Easy

What is the main characteristic of Stochastic Gradient Descent?

💡 Hint: Consider its speed and randomness.

4 more questions available

Interactive Quizzes

Quick quizzes to reinforce your learning

Question 1

What method uses the entire dataset for optimization?

Stochastic Gradient Descent
Batch Gradient Descent
Mini-batch Gradient Descent

💡 Hint: Think about how data is used in updates.

Question 2

Stochastic Gradient Descent relies on which principle?

True
False

💡 Hint: Remember its characteristic method.

2 more questions available

Challenge Problems

Push your limits with advanced challenges

Challenge 1 Hard

You are given a dataset with 100,000 examples. Explain the advantages of using Mini-batch Gradient Descent over Batch or Stochastic Gradient Descent.

💡 Hint: Consider the computational efficiency and speed.

Challenge 2 Hard

Analyze a scenario where you would choose Stochastic Gradient Descent over Mini-batch Gradient Descent. What factors influence this decision?

💡 Hint: Focus on the data characteristics and speed requirements.

Get performance evaluation

Reference links

Supplementary resources to enhance your learning experience.