Practice Optimization in Deep Learning - 2.7 | 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 loss surface?

πŸ’‘ Hint: Think about how this impacts optimization.

Question 2

Easy

Explain what vanishing gradients are.

πŸ’‘ Hint: Consider the effect on learning speed.

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 do vanishing gradients lead to in deep learning?

  • Slower learning
  • Faster convergence
  • More local minima

πŸ’‘ Hint: Consider what happens when gradients stop changing significantly.

Question 2

True or False: Exploding gradients can cause numerical instability in a model.

  • True
  • False

πŸ’‘ Hint: Recall how weight adjustments impact model training.

Solve 1 more question and get performance evaluation

Challenge Problems

Push your limits with challenges.

Question 1

You are training a deep neural network, and you notice that it gets stuck frequently during the training process. Outline a multi-step strategy to improve the situation, considering initialization, normalization, and architecture.

πŸ’‘ Hint: Remember the key optimization techniques we've discussed.

Question 2

Imagine a scenario where your model suffers from both exploding and vanishing gradients. How would you design your optimization approach to counter both issues?

πŸ’‘ Hint: Draw from our discussion about the various techniques.

Challenge and get performance evaluation