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How artificial neurons learns?
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An auto-associative network is: a) a neural network that contains no loops b) a neural network that contains feedback c) a neural network that has only one loop d) a single layer feed-forward neural network with pre-processing
What is back propagation? a) It is another name given to the curvy function in the perceptron b) It is the transmission of error back through the network to adjust the inputs c) It is the transmission of error back through the network to allow weights to be adjusted so that the network can learn. d) None of the mentioned
What are neural networks and how do they relate to ai?
How are nns related to statistical methods?
Describe the structure of artificial neural networks?
What can you do with an nn and what not?
What is the difference between a Feedforward Neural Network and Recurrent Neural Network?
How are layers counted?
How neural networks became a universal function approximators?
Which of the following is not the promise of artificial neural network? a) It can explain result b) It can survive the failure of some nodes c) It has inherent parallelism d) It can handle noise
What are batch, incremental, on-line, off-line, deterministic, stochastic, adaptive, instantaneous, pattern, constructive, and sequential learning?
How many kinds of nns exist?
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