Practice Forward Propagation (8.2.2) - Deep Learning and Neural Networks

Practice: Forward Propagation

Lesson 8.2.2Deep Learning and Neural Networks

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9 questions

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6 sample questions

Question 1.
What is forward propagation?
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Consider the flow of data through the layers.
Question 2.
Name an activation function.
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Think about functions that introduce non-linearity.
3Question 3.
What does forward propagation do in a neural network?
- A. It trains the model
- B. It propagates input data through the network to produce an output
- C. It evaluates model performance
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Remember the flow of input to output.
4Question 4.
True or False: Activation functions are used to make the outputs linear.
- True
- False
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Consider the purpose of activation functions.
Question 5.
Design a simple neural network with two hidden layers. Describe how forward propagation occurs with an example input and weights.
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Focus on how each layer transforms the data.
Question 6.
Compare and contrast the use of Sigmoid and ReLU in forward propagation. Under what circumstances would you choose one over the other?
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Consider the architectural implications of selecting activation functions.

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