Practice Training: Gradient Descent + Backpropagation (1.4)

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8 questions

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6 sample questions

Question 1.
What is gradient descent used for?
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Think about what happens during the training process of a neural network.
Question 2.
Define backpropagation.
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Consider the role of gradients in adjusting weights.
3Question 3.
What does gradient descent aim to minimize?
- Training time
- Weights
- Loss function
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Recall the primary goal during the training process.
4Question 4.
True or False: Backpropagation is not necessary for training deep neural networks.
- True
- False
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Consider the function of backpropagation in neural network training.
Question 5.
Design a scenario where gradient descent might fail to converge and explain why.
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Consider scenarios where the landscape of the loss function leads to multiple paths.
Question 6.
Explain how you would implement backpropagation for a neural network with three hidden layers.
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Think about how layers interact in terms of weights and outputs.

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