Practice Regularization (6.3) - Deep Learning Architectures - Artificial Intelligence Advance

Practice: Regularization

Lesson 6.3Deep Learning Architectures

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10 questions

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6 sample questions

Question 1.
What is overfitting in machine learning?
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Think about the model's performance on unseen data.
Question 2.
What are the two types of regularization mentioned?
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Recall the names given for each type during the lesson.
3Question 3.
What is the primary purpose of regularization in machine learning?
- To enhance underfitting
- To reduce overfitting
- To avoid bias
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Think about what happens when a model loses its ability to generalize.
4Question 4.
True or False: L1 regularization can result in some weights being exactly zero.
- True
- False
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Recall the penalty applied in L1 regularization.
Question 5.
Consider a neural network that exhibits significant overfitting during training. Propose a strategy combining L1 and L2 regularization to improve its performance.
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How can combining penalties help create a stronger model?
Question 6.
Explain how you would implement dropout in a training regime for a deep learning model with several layers to balance robustness and performance.
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How do you ensure the model can still learn effectively while using dropout?

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