Perceptron can learn:
(A) AND
(B) XOR
(C) Both A and B
(D) None of these
Answers
Explanation:
To build up towards the (useful) multi-layer Neural Networks, we will start with considering the (not really useful) single-layer Neural Network. This is called a Perceptron.
The Perceptron
Input is multi-dimensional (i.e. input can be a vector):
input x = ( I1, I2, .., In)
Input nodes (or units) are connected (typically fully) to a node (or multiple nodes) in the next layer.
A node in the next layer takes a weighted sum of all its inputs:
Summed input =
Example
input x = ( I1, I2, I3) = ( 5, 3.2, 0.1 )
Summed input = = 5 w1 + 3.2 w2 + 0.1 w3
The rule
The output node has a "threshold" t.
Rule: If summed input ≥ t, then it "fires" (output y = 1).
Else (summed input < t) it doesn't fire (output y = 0).
This implements a function
Obviously this implements a simple function from multi-dimensional real input to binary output. What kind of functions can be represented in this way?
We can imagine multi-layer networks. Output node is one of the inputs into next layer.
Perceptron has just 2 layers of nodes (input nodes and output nodes). Often called a single-layer network on account of having 1 layer of links, between input and output.