How neural networks became a universal function approximators?
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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
Neural Networks are complex ______________ with many parameters. a) Linear Functions b) Nonlinear Functions c) Discrete Functions d) Exponential Functions
what are some advantages and disadvantages of neural network?
What is the role of activation functions in a Neural Network?
How does ill-conditioning affect nn training?
What are the applications of a Recurrent Neural Network (RNN)?
How are weights initialized in a network?
What is backprop?
What are the population, sample, training set, design set, validation set, and test set?
A perceptron is: a) a single layer feed-forward neural network with pre-processing b) an auto-associative neural network c) a double layer auto-associative neural network d) a neural network that contains feedback
What are batch, incremental, on-line, off-line, deterministic, stochastic, adaptive, instantaneous, pattern, constructive, and sequential learning?
What is Pooling in CNN and how does it work?
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