Practice Convex and Non-Convex Optimization - 2.2 | 2. Optimization Methods | Advance Machine Learning
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Practice Questions

Test your understanding with targeted questions related to the topic.

Question 1

Easy

Define a convex function.

πŸ’‘ Hint: Think about the properties of the graph of a function.

Question 2

Easy

What does a local minimum mean?

πŸ’‘ Hint: Consider how it compares to the rest of the function.

Practice 4 more questions and get performance evaluation

Interactive Quizzes

Engage in quick quizzes to reinforce what you've learned and check your comprehension.

Question 1

What is a characteristic of a convex function?

  • It has multiple local minima
  • A line segment between any two points lies below the graph
  • A line segment between any two points lies above or on the graph

πŸ’‘ Hint: Think about the direction of the line segment when plotted.

Question 2

True or False: Non-convex functions guarantee a global minimum.

  • True
  • False

πŸ’‘ Hint: Remember the definition of non-convexity.

Solve 1 more question and get performance evaluation

Challenge Problems

Push your limits with challenges.

Question 1

Suppose you have a neural network with a complex loss surface. Discuss strategies to mitigate the problem of getting stuck in local minima.

πŸ’‘ Hint: Consider recent advancements in optimization algorithms.

Question 2

Given a convex function, describe the steps you would take to optimize it using Gradient Descent.

πŸ’‘ Hint: Think about the iterative process and key settings involved.

Challenge and get performance evaluation