Interested to Buy Any Domain ? << Click Here >> for more details...
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
- 1 Answers
- 4662 Views
- I also Faced
- E-Mail Answers
Post New Answer View All Answers
How are weights initialized in a network?
Why use artificial neural networks? What are its advantages?
What are the disadvantages of artificial neural networks?
How to avoid overflow in the logistic function?
How human brain works?
What are the population, sample, training set, design set, validation set, and test set?
What is the role of activation functions in a Neural Network?
What can you do with an nn and what not?
What are neural networks? What are the types of neural networks?
Explain neural networks?
what are some advantages and disadvantages of neural network?
How are nns related to statistical methods?
How many kinds of nns exist?
Explain Generative Adversarial Network.
What are neural networks and how do they relate to ai?
