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Test your understanding with targeted questions related to the topic.
Question 1
Easy
What is a non-convex loss surface?
π‘ Hint: Think about how this impacts optimization.
Question 2
Easy
Explain what vanishing gradients are.
π‘ Hint: Consider the effect on learning speed.
Practice 4 more questions and get performance evaluation
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?
π‘ Hint: Consider what happens when gradients stop changing significantly.
Question 2
True or False: Exploding gradients can cause numerical instability in a model.
π‘ Hint: Recall how weight adjustments impact model training.
Solve 1 more question and get performance evaluation
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.
π‘ 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?
π‘ Hint: Draw from our discussion about the various techniques.
Challenge and get performance evaluation