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 7882Neural Networks are complex ______________ with many parameters. a) Linear Functions b) Nonlinear Functions c) Discrete Functions d) Exponential Functions
1 12773A 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 6863The name for the function in question 16 is a) Step function b) Heaviside function c) Logistic function d) Perceptron function
1 4456Having 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 4232The 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 12117Which 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 7394Post New AI Neural Networks Questions
What is artificial intelligence neural networks?
What is backprop?
How human brain works?
Explain Generative Adversarial Network.
What is a Neural Network?
What learning rate should be used for backprop?
How artificial neurons learns?
List some commercial practical applications of artificial neural networks?
Why use artificial neural networks? What are its advantages?
What are batch, incremental, on-line, off-line, deterministic, stochastic, adaptive, instantaneous, pattern, constructive, and sequential learning?
How artificial neural networks can be applied in future?
Which is the similar operation performed by the drop-out in neural network?
How to avoid overflow in the logistic function?
What are artificial neural networks?
What is the advantage of pooling layer in convolutional neural networks?
