Neural Network and Learning Machine

Important Questions -

[5 Marks Questions]

1. Comparison between Artificial Neural Network and Biological Neural Network.
2. Describe the architecture of Artificial Neural Network and Biological neural network.
3. What is Activation function and what are the different types of Activation function.
4. Explain Supervised and Unsupervised learning in Neural Network.
5. Explain in detail Feed-Forward Neural Network.
6. What is Perceptron, explain Perceptron as Classifier with AND, OR & XOR examples.
7. Explain Perceptron Learning Algorithm and Perceptron Learning Rules.
8. What are the limitations of Perceptron learning.
9. What are Auto-Associative and Hetero-Associative Neural Network.
10. Explain Convergence Theorem.
11. Explain XOR-Problem in detail.
12. Explain Backpropagation learning in detail.
13. Define Cross-Validation. What are the different types of cross-validation.
14. Differentiate between (neural) network-growing and network-pruning. Explain Hessian based network pruning method.
15. Explain conjugate-gradient back propagation learning algorithm, with the help of conjugate vectors.

[2 Marks Questions]

1. What is Neural Network.
2. What are the different types of Activation function.
3. Differentiate between Linear Neurons and Non-linear Neurons.
4. What are the basic learning laws.
5. What is basic functionality of Axons in Human brain.
6. What is Perceptron Model.
7. Write and briefly describe cross-validation.
8. Write down any two limitations of back propagation network algorithm.
9. What is the role of hidden layers in a neural network.
10. How are the artificial neural networks different from normal computer.
11. List some commercial practical application of artificial neural network.
12. What id the main principle of kernel regression.
13. How you define the RBF network.
14. What is the Dynamic system.
15. Give the concept of associative memory.

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