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Artificial Neural Networks
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Artificial Neural Networks
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25 Questions

1. Perceptron Theorem

2. For MLPs and backprop and perhaps simple perceptrons how is the change in the weights determined?

3. Learning as Optimization

4. State the Kohonen learning rule for neural networks

5. What is a perceptron?

6. Formal definition of the associative memory problem

7. Neural Network Applications

8. Name the condition for the strengthning of synaptic connection between two cells

9. Artificial Neural Network

10. What is unsupervised learning?

11. Energy Minimization

12. What's the significance of the cost function?

13. What are attractors?

14. State Oja's rule for unsupervised learning

15. What are the characteristics of the learning constant?

16. What is an epoch?

17. For MCPs the new weight equals..?

18. What are the similarities between biological neural networks and artificial neural networks?

19. How do Neural Networks Learn?

20. What is optimization done with respect to?

21. What are some applications of ANN?

22. In Oja's rule what's the limiting term and what does it do?

23. What is the ultimate objective of training?

24. State the differences between the Kohonen rule and the competitive learning rule

25. Learning can be perceived as what?

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