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
How many kinds of kohonen networks exist?
What is Pooling in CNN and how does it work?
How to avoid overflow in the logistic function?
What learning rate should be used for backprop?
List some commercial practical applications of artificial neural networks?
Are neural networks helpful in medicine?
What can you do with an nn and what not?
A 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
What is artificial intelligence neural networks?
What is a Neural Network?
What is a neural network and what are some advantages and disadvantages of such a network?
What is simple artificial neuron?