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What are batch, incremental, on-line, off-line, deterministic, stochastic, adaptive, instantaneous, pattern, constructive, and sequential learning?
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How artificial neurons learns?
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What are the applications of a Recurrent Neural Network (RNN)?
Explain neural networks?
How many kinds of kohonen networks exist?
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. a) True b) False c) Sometimes – it can also output intermediate values as well d) Can’t say
What is the difference between a Feedforward Neural Network and Recurrent Neural Network?
What can you do with an nn and what not?
Describe the structure of artificial neural networks?
Explain Generative Adversarial Network.
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
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
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