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Describe the structure of artificial neural networks?
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How human brain works?
What learning rate should be used for backprop?
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 can you do with an nn and what not?
How are nns related to statistical methods?
Why is the XOR problem exceptionally interesting to neural network researchers? a) Because it can be expressed in a way that allows you to use a neural network b) Because it is complex binary operation that cannot be solved using neural networks c) Because it can be solved by a single layer perceptron d) Because it is the simplest linearly inseparable problem that exists.
How does ill-conditioning affect nn training?
What is the role of activation functions in a Neural Network?
How artificial neurons learns?
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?
What are cases and variables?
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