Practice Non-Convex Optimization - 2.2.2 | 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

What is a non-convex function?

πŸ’‘ Hint: Think about the landscapes created by the graphs of these functions.

Question 2

Easy

What is a local minimum?

πŸ’‘ Hint: Relate it to the concept of peaks and valleys.

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 non-convex functions?

  • They have a single global minimum.
  • They have multiple local minima and saddle points.
  • They are always easier to optimize.

πŸ’‘ Hint: Consider how these functions behave differently from convex functions.

Question 2

True or False: A saddle point is a minimum point.

  • True
  • False

πŸ’‘ Hint: Focus on the definition of saddle points and how they behave.

Solve and get performance evaluation

Challenge Problems

Push your limits with challenges.

Question 1

Propose a strategy for navigating a non-convex optimization landscape while training a neural network.

πŸ’‘ Hint: Think about methods that help keep the learning process dynamic.

Question 2

Imagine you are training a reinforcement learning agent. Describe how it might encounter non-convex optimization issues.

πŸ’‘ Hint: Consider scenarios where the agent can make multiple choices but may end up being suboptimal.

Challenge and get performance evaluation