Practice Fundamentals Of Neural Networks (8.1) - Deep Learning and Neural Networks

Practice: Fundamentals of Neural Networks

Lesson 8.1Deep Learning and Neural Networks

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9 questions

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6 sample questions

Question 1.
What does an artificial neural network (ANN) mimic?
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Think about biological structures.
Question 2.
Name the three types of layers in a neural network.
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Recall the purpose of each layer.
3Question 3.
What does the Sigmoid activation function do?
- Outputs the input value
- Squashes input to a range between 0 and 1
- Returns the max of 0 and input
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Think about its application in binary classification.
4Question 4.
True or False: The ReLU function can output negative values.
- True
- False
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Recall the definition of ReLU.
Question 5.
Design a simple neural network architecture for a binary classification problem. Justify the number of layers and activation functions you would use.
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Think about how many features you may have and what your output needs to be.
Question 6.
Explain the vanishing gradient problem and how activation functions like ReLU can help mitigate it.
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Consider the implications of different activation functions on the gradient.

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