Practice Backpropagation And Activation Functions (7.3) - Deep Learning and Neural Networks

Practice: Backpropagation and Activation Functions

Lesson 7.3Deep Learning and Neural Networks

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9 questions

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6 sample questions

Question 1.
What is the purpose of backpropagation in neural networks?
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Think about how the network learns.
Question 2.
Name one common activation function.
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Recall the functions we discussed.
3Question 3.
What is the main purpose of backpropagation?
- To reduce the loss during training
- To increase the complexity of the model
- To select optimal activation functions
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Remember its role in training.
4Question 4.
True or False: The Sigmoid function can cause the vanishing gradient problem.
- True
- False
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Consider the effects of squashing outputs.
Question 5.
Given the Sigmoid function outputs a value of 0.7 for a certain input. If the true output is 0, calculate the Mean Squared Error (MSE) loss.
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Remember the MSE formula involves squaring the difference.
Question 6.
If using gradient descent, explain how learning rate affects the weight update process during backpropagation. What happens if it's too high or too low?
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Think about speed versus accuracy in updates.

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