APPLICATION OF SUPERVISED LEARNING
NEURAL NETWORK
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Question
[CLICK ON ANY CHOICE TO KNOW THE RIGHT ANSWER]
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It has set of nodes and connections.
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Each node computes it’s weighted input.
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Node could be in excited state or non-excited state.
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All of the mentioned.
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Detailed explanation-1: -Explanation: Pattern recognition is what single layer neural networks are best at but they don’t have the ability to find the parity of a picture or to determine whether two shapes are connected or not. 9. Which is true for neural networks? Explanation: All mentioned are the characteristics of neural network.
Detailed explanation-2: -11. Which of the following is true for neural networks? Explanation: Neural networks has a set of nodes and connections where each node computes it’s weighted input and a node could be in an excited state or non-excited state. So all of the above is correct.
Detailed explanation-3: -Explanation: Neural networks are complex linear functions with many parameters. Take Artificial Intelligence Mock Tests-Chapterwise! 6. A perceptron adds up all the weighted inputs it receives, and if it exceeds a certain value, it outputs a 1, otherwise it just outputs a 0.