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Practice Questions
Test your understanding with targeted questions related to the topic.
Question 1
Easy
What is a Perceptron?
💡 Hint: Think about its structure and function.
Question 2
Easy
List the three components of a Multi-Layer Neural Network.
💡 Hint: Recall what layers make up the architecture.
Practice 4 more questions and get performance evaluation
Interactive Quizzes
Engage in quick quizzes to reinforce what you've learned and check your comprehension.
Question 1
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
💡 Hint: Think about the XOR problem.
Question 2
True or False: Multi-layer Neural Networks can solve non-linear problems.
- True
- False
💡 Hint: Recall the advantages of having multiple layers.
Solve 1 more question and get performance evaluation
Challenge Problems
Push your limits with challenges.
Question 1
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.
💡 Hint: Look for patterns in the data representation.
Question 2
Design a simple Multi-Layer Neural Network to identify handwritten digits. Explain your layer configuration and the purpose of each layer.
💡 Hint: Think about how the image data transforms at each layer.
Challenge and get performance evaluation