Practice Popular Models (4.3) - Deep Learning Architectures

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10 questions

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6 sample questions

Question 1.
What does CNN stand for?
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Think about networks specialized for images.
Question 2.
What type of data do RNNs primarily handle?
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Consider sequences like time series or language.
3Question 3.
What is the primary function of pooling layers in CNNs?
- Feature extraction
- Downsampling
- Data augmentation
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What do pooling layers do to the spatial size?
4Question 4.
True or False: LSTMs can help mitigate the vanishing gradient problem.
- True
- False
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Think of what LSTMs are capable of regarding memory.
Question 5.
Imagine you are designing a new kind of neural network for image classification. Discuss how you would incorporate the strengths of CNNs, RNNs, and Transformers into your model.
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Think about how you can leverage each model's unique advantages.
Question 6.
Illustrate a scenario where GANs could be used creatively. Describe the roles of the generator and the discriminator in your scenario.
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Consider how both components enhance each other's abilities.

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