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 7397Neural Networks are complex ______________ with many parameters. a) Linear Functions b) Nonlinear Functions c) Discrete Functions d) Exponential Functions
1 12156A 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 6433The name for the function in question 16 is a) Step function b) Heaviside function c) Logistic function d) Perceptron function
1 4095Having 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 3812The 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 11607Which 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 6902Post New AI Neural Networks Questions
What are conjugate gradients, levenberg-marquardt, etc.?
What is artificial intelligence neural networks?
Why use artificial neural networks? What are its advantages?
What is the role of activation functions in a Neural Network?
How many kinds of kohonen networks exist?
What are batch, incremental, on-line, off-line, deterministic, stochastic, adaptive, instantaneous, pattern, constructive, and sequential learning?
How neural networks became a universal function approximators?
What learning rate should be used for backprop?
How does an LSTM network work?
What is backprop?
Describe the structure of artificial neural networks?
what are some advantages and disadvantages of neural network?
Which is the similar operation performed by the drop-out in neural network?
What is simple artificial neuron?
How are nns related to statistical methods?
