Practice Gradient Descent: The Fundamental Principle (11.5.1) - Introduction to Deep Learning (Weeks 11)
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

Gradient Descent: The Fundamental Principle

Practice - Gradient Descent: The Fundamental Principle

Learning

Practice Questions

Test your understanding with targeted questions

Question 1 Easy

What is Gradient Descent?

💡 Hint: Think about how we adjust to find the lowest point in a valley.

Question 2 Easy

How does the learning rate affect the training of a neural network?

💡 Hint: Consider the effects of moving too fast or too slow.

4 more questions available

Interactive Quizzes

Quick quizzes to reinforce your learning

Question 1

What does Gradient Descent aim to do?

Maximize Loss
Minimize Loss
Keep Loss Constant

💡 Hint: Think about the goals of training a model.

Question 2

True or False: A high learning rate can lead to slower convergence.

True
False

💡 Hint: Remember how rapid movement affects stability.

1 more question available

Challenge Problems

Push your limits with advanced challenges

Challenge 1 Hard

Calculate the optimal learning rate for a neural network model given specific training data characteristics.

💡 Hint: Consider starting with standard values like 0.001, 0.01, and 0.1.

Challenge 2 Hard

Discuss the potential issues and benefits of manually tuning the learning rate versus using adaptive learning rate methods.

💡 Hint: What advantages do automated adjustments provide in your experience?

Get performance evaluation

Reference links

Supplementary resources to enhance your learning experience.