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Machine Learning: Introduction to Neural Network
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Machine Learning: Introduction to Neural Network
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25 Questions

1. What is the name of node which take binary values TRUE (T) and FALSE (F)?
2. What is full form of ANNs?
3. Identify the following activation function :
φ(V) = Z + (1/ 1 + exp (.
4. Which of the following is not the promise of artificial neural network?
5. Why do we need biological neural networks?
6. What's the main point of difference between human & machine intelligence?
7. Back propagation is a learning technique that adjusts weights in the neural network by propagating weight changes.
8. Which of the following is an Applications of Neural Networks?
9. Example of a unsupervised feature map?
10. What is auto-association task in neural networks?
11. Which of the following options is correct?
12. Which of the following model has ability to learn?
13. What is an auto-associative network?
14. What is the full form of BN in Neural Networks?
15. What is Neuro software?
16. Does for feature mapping there's need of supervised learning?
17. Who was the inventor of the first neurocomputer?
18. The BN variables are composed of how many dimensions?
19. Does McCulloch-pitts model have ability of learning?
20. Does pattern classification & grouping involve same kind of learning?
21. When both inputs are different, what will be the output of the above figure?
22. Does pattern classification belongs to category of non-supervised learning?
23. A 4-input neuron has weights 1, 2, 3 and 4. The transfer function is linear with the constant of proportionality being equal to 2. The inputs are 4, 3, 2 and 1 respectively. What will be the output?
24. Slots and facets are used in
25. An artificial neuron receives n inputs x1, x2, x3.

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