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
1 7362Neural Networks are complex ______________ with many parameters. a) Linear Functions b) Nonlinear Functions c) Discrete Functions d) Exponential Functions
1 12123A 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
1 6404The name for the function in question 16 is a) Step function b) Heaviside function c) Logistic function d) Perceptron function
1 4076Having 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
1 3774The network that involves backward links from output to the input and hidden layers is called as ____. a) Self organizing maps b) Perceptrons c) Recurrent neural network d) Multi layered perceptron
1 11571Which of the following is an application of NN (Neural Network)? a) Sales forecasting b) Data validation c) Risk management d) All of the mentioned
1 6878Post New AI Neural Networks Questions
How human brain works?
How are artificial neural networks different from normal computers?
What are neural networks and how do they relate to ai?
How neural networks became a universal function approximators?
What are neural networks? What are the types of neural networks?
How are weights initialized in a network?
Describe the structure of artificial neural networks?
What can you do with an nn and what not?
What is backprop?
How many kinds of kohonen networks exist?
How artificial neurons learns?
How to avoid overflow in the logistic function?
What are the applications of a Recurrent Neural Network (RNN)?
List some commercial practical applications of artificial neural networks?
Explain Generative Adversarial Network.