The document discusses perceptron training and neural network training. It provides examples of initializing weights for an AND operation and sample input/output pairs. It also shows initializing weights for another example and calculating the summation and output for different inputs. The document discusses training a neural network by starting with random weights, loading training examples to compute the output, modifying the weights to reduce error between computed and observed outputs, iterating over all examples, and terminating when weights stop changing or error is small.

1. 6/10/20136/10/2013
Perceptron Training
• Algorithm
While epoch produces a non null errorWhile epoch produces a non null error
End WhileEnd While
Perceptron Training
• Assuming that f is the threshold function
• What are the weight values?
• Initialize with random weight values
t =t = 00..00
yy
xx
--11
WW11 = ?= ?
WW33 = ?= ?
WW22 = ?= ?
ANDAND
A B OutputA B Output
0 0 00 0 0
0 1 00 1 0
1 0 01 0 0
1 1 11 1 1

2. 66//1010//20132013
Perceptron Training
t =t = 00..00
yy
xx
--11
WW11 = ?= ?
WW33 = ?= ?
WW22 = ?= ?
ANDAND
A B OutputA B Output
0 0 00 0 0
0 1 00 1 0
1 0 01 0 0
1 1 11 1 1
I1 I2 I3 Summation Output
-1 0 0 (-1*0.3) + (0*0.5) + (0*-0.4) = -0.3 0
-1 0 1 (-1*0.3) + (0*0.5) + (1*-0.4) = -0.7 0
-1 1 0 (-1*0.3) + (1*0.5) + (0*-0.4) = 0.2 1
-1 1 1 (-1*0.3) + (1*0.5) + (1*-0.4) = -0.2 0
Lanjutkan…!
Training in Neural Network
• Learn values of weights from I/O pairs
• Start with random weights
• Load training example’s input
• Observe computed input
• Modify weights to reduce difference
• Iterate over all training examples
• Terminate when weights stop changing
OR when error is very small