Practice From Perceptron To Multi-layer Neural Networks (7.2) - Deep Learning and Neural Networks

Practice: From Perceptron to Multi-layer Neural Networks

Lesson 7.2Deep Learning and Neural Networks

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9 questions

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6 sample questions

Question 1.
What is a Perceptron?
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Think about its structure and function.
Question 2.
List the three components of a Multi-Layer Neural Network.
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Recall what layers make up the architecture.
3Question 3.
What is the primary limitation of the Perceptron?
- It can only process large datasets
- It only works for linearly separable problems
- It uses multiple layers
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Think about the XOR problem.
4Question 4.
True or False: Multi-layer Neural Networks can solve non-linear problems.
- True
- False
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Recall the advantages of having multiple layers.
Question 5.
You are given a dataset that includes both linear and non-linear classes. Explain how you would determine whether to use a Perceptron or a Multi-Layer Neural Network to solve this classification problem.
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Look for patterns in the data representation.
Question 6.
Design a simple Multi-Layer Neural Network to identify handwritten digits. Explain your layer configuration and the purpose of each layer.
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Think about how the image data transforms at each layer.

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