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Neural Networks MCQ

  1. What is adaline in neural networks?
  2. Which is true for neural networks?
  3. What are models in neural networks?
  4. How many types of Artificial Neural Networks?
  5. What does RNN Stands for?
  6. What is auto-association task in neural networks?
  7. What is plasticity in neural networks?
  8. Signal transmission at synapse is a?
  9. Operations in the neural networks can perform what kind of operations?
  10. A neural network is a network or circuit of neurons.
  11. Neural networks can be used in different fields. such as -
  12. What are the types Of Neural Networks?
  13. Which of the following option is not the disadvantage of Recurrent Neural Network?
  14. Neural Networks consist of artificial neurons that are similar to the biological model of neurons.
Last Updated: Feb 07, 2023,
Posted in Interview Questions,
20 Questions
Neural Networks MCQ

Practice Best Neural Networks MCQ Test & Online Quiz

We have listed below the best Neural Networks MCQ Questions, that check your basic knowledge of Neural Networks. This Neural Networks MCQ Test contains 20 Multiple Choice Questions. You have to select the right answer to check your final preparation for the Neural Networks MCQ Exam/Interviews. apart from this, you can also download the Neural Networks MCQ PDF, completely free from the link given below.

  • adaptive line element
  • adaptive linear element
  • automatic linear element
  • none of the mentioned
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  • It has set of nodes and connections
  • Each node computes it’s weighted input
  • Node could be in excited state or non-excited state
  • All of the above
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  • representation of biological neural networks
  • mathematical representation of our understanding
  • both first & second
  • none of the above
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  • Recurrent Neural Network
  • Recurring Neural Network
  • Removable Neural Network
  • None of the above
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  • predicting the future inputs
  • related to storage & recall task
  • find relation between 2 consecutive inputs
  • all of the above
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  • input pattern has become static
  • input pattern keeps on changing
  • output pattern keeps on changing
  • none of the above
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  • chemical process
  • physical process
  • both chemical & physical process
  • none of the above
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  • parallel
  • serial
  • both parallel & serial
  • none of the above
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  • Classification
  • Data processing
  • Compression.
  • All of the above
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  • Feed-forward Neural Network
  • Radial Basis Functions (RBF) Neural Network
  • Recurrent Neural Network
  • All of the above
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  • Training an RNN is quite a challenging task
  • Inputs of any length can be processed in this model.
  • Exploding and gradient vanishing is common in this model.
  • It cannot process very long sequences if using 'tanh' or 'relu' as an activation function
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  • Convolution Neural Network
  • Recurrent Neural Network
  • Modular Neural Network
  • Radial Basis Functions Neural Network
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  • Multilayer Perceptron
  • Kohonen SOM
  • Radial Basis Function Network
  • All of the above
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  • It can be performed without any problem
  • It can be implemented in any application.
  • A neural network learns and reprogramming is not necessary
  • All of the above
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