Practice Regularization Techniques (8.4) - Deep Learning and Neural Networks

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6 sample questions

Question 1.
What is the purpose of dropout in neural networks?
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Think about how it affects neuron collaboration.
Question 2.
Define L1 regularization.
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Consider the effect on model complexity.
3Question 3.
What does dropout do in a neural network?
- A) Increases the number of neurons
- B) Randomly disables neurons during training
- C) Reduces the training set size
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Consider its role in preventing overfitting.
4Question 4.
True or False: L2 regularization always sets weights to zero.
- True
- False
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Think about the nature of the L2 penalty.
Question 5.
Devise a plan to experimentally test the effectiveness of dropout vs. L2 regularization on the same dataset.
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Consider the models' architecture and validation metrics you will employ.
Question 6.
Evaluate the impact of setting a high penalty in L1 regularization on specific types of datasets.
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Reflect on model interpretability versus complexity.

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