Q.1
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.
Q.2
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
Q.3
Why are linearly separable problems of interest of neural network researchers?
  • a) Because they are the only class of problem that network can solve successfully
  • b) Because they are the only class of problem that Perceptron can solve successfully
  • c) Because they are the only mathematical functions that are continue
  • d) Because they are the only mathematical functions you can draw
Q.4
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
Q.5
Neural Networks are complex ______________ with many parameters.
  • a) Linear Functions
  • b) Nonlinear Functions
  • c) Discrete Functions
  • d) Exponential Functions
Q.6
A perceptron adds up all the weighted inputs it receives, and if it exceeds a certain value, it outputs aotherwise it just outputs a 0.
  • a) True
  • b) False
  • c) Sometimes – it can also output intermediate values as well
  • d) Can’t say
Q.7
What is the name of the function in the following statement “A perceptron adds up all the weighted inputs it receives, and if it exceeds a certain value, it outputs aotherwise it just outputs a 0”?
  • a) Step function
  • b) Heaviside function
  • c) Logistic function
  • d) Perceptron function
Q.8
Having multiple perceptrons can actually solve the XOR problem satisfactorily: this is because each perceptron can partition off a linear part of the space itself, and they can then combine their results.
  • a) True – this works always, and these multiple perceptrons learn to classify even complex problems
  • b) False – perceptrons are mathematically incapable of solving linearly inseparable functions, no matter what you do
  • c) True – perceptrons can do this but are unable to learn to do it – they have to be explicitly hand-coded
  • d) False – just having a single perceptron is enough
Q.9
The network that involves backward links from output to the input and hidden layers is called _________
  • a) Self organizing maps
  • b) Perceptrons
  • c) Recurrent neural network
  • d) Multi layered perceptron
Q.10
Which of the following is an application of NN (Neural Network)?
  • a) Sales forecasting
  • b) Data validation
  • c) Risk management
  • d) All of the mentioned
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