This document provides an overview of neural network learning techniques including supervised, unsupervised, and reinforcement learning. It discusses the Hebbian learning rule, which updates weights based on the activation of connected neurons. Examples are provided to illustrate how the Hebbian rule can be used to train networks without error signals by detecting correlations in input-output patterns.

1. Neural Networks
Dr. Randa Elanwar
Lecture 7

2. Lecture Content
• NN Learning techniques:
– Supervised learning
– Unsupervised learning
– Reinforcement learning
• Other learning laws: Hebbian learning rule
2Neural Networks Dr. Randa Elanwar

3. Basic models of ANN
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Basic Models of
ANN
Activation
function
Interconnections Learning rules

4. Learning
• It’s a process by which a NN adapts itself to a
stimulus by making proper parameter adjustments,
resulting in the production of desired response
• Two kinds of learning
– Parameter learning:- connection weights are updated
– Structure Learning:- change in network structure
4Neural Networks Dr. Randa Elanwar

5. Training
• The process of modifying the weights in the
connections between network layers with the
objective of achieving the expected output is called
training a network.
• This is achieved through
– Supervised learning
– Unsupervised learning
– Reinforcement learning
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6. Supervised Learning
• Child learns from a teacher
• Each input vector requires a corresponding target vector.
• Training pair=[input vector, target vector]
Neural
Network
W
Error
Signal
Generator
X
(Input)
Y
(Actual output)
(Desired Output)
Error
(D-Y)
signals
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7. Supervised learning
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Supervised learning does
minimization of error

8. Supervised Learning
• Learning is performed by presenting pattern with target
• During learning, produced output is compared with the
desired output
– The difference between both output is used to modify
learning weights according to the learning algorithm
• Recognizing hand-written digits, pattern recognition and etc.
• Neural Network models: perceptron, feed-forward, radial
basis function, support vector machine.
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9. Unsupervised Learning
• How a fish or tadpole learns
• All similar input patterns are grouped together as clusters.
• If a matching input pattern is not found a new cluster is
formed
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10. Unsupervised Learning
• Targets are not provided
• Appropriate for clustering task
– Find similar groups of documents in the web, content
addressable memory, clustering.
• Neural Network models: Kohonen, self organizing maps,
Hopfield networks.
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11. Self-organizing
• In unsupervised learning there is no error feedback
• Network must discover patterns, regularities,
features for the input data over the output
• While doing so the network might change in
parameters
• This process is called self-organizing
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12. •Target is provided, but the desired output is absent.
•The net is only provided with guidance to determine the
produced output is correct or vise versa.
•Weights are modified in the units that have errors
Reinforcement Learning
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13. Reinforcement Learning
NN
W
Error
Signal
Generator
X
(Input)
Y
(Actual output)
Error
signals R
Reinforcement signal
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14. When Reinforcement learning is used?
• If less information is available about the target output
values (critic information)
• Learning based on this critic information is called
reinforcement learning and the feedback sent is called
reinforcement signal
• Feedback in this case is only evaluative and not instructive
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15. Some learning algorithms we will learn are
• Supervised:
• Adaline, Madaline
• Perceptron
• Back Propagation
• multilayer perceptrons
• Radial Basis Function Networks
• Unsupervised
• Competitive Learning
• Kohenen self organizing map
• Learning vector quantization
• Hebbian learning
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16. Some learning algorithms we will learn are
• The only NN learning rule we have studied so far is the
“perceptron learning” also known as “Error-Correction
learning” or “delta rule”.
• The learning signal is equal to the error signal: difference
between the desired and the actual neuron output
• We will consider also “Hebbian learning” and “Competitive
learning”
16Neural Networks Dr. Randa Elanwar

17. Hebbian learning Rule
• Feedforward unsupervised learning also known as “coincidence learning”
• The learning signal is equal to the neuron’s output
• To update the weights of a neuron i, the inputs to neuron i come from a
preceding neuron j (xj)
wi,j = wi,j + wi,j
wi,j =  * outputi * inputj
wi,j =  oixj
• It is clear that Hebbian learning is not going to get our Perceptron to learn a set
of training data, since weight changes depend only on the actual outputs and
we don’t have desired outputs to compare to.
•  Hebb rule can be used for pattern association, pattern categorization, pattern
classification and over a range of other areas
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18. Hebbian learning Rule
• If oixj is positive the results is increase in weight else vice versa
• In other words:
1. If two neurons on either side of a connection are activated
simultaneously (i.e. synchronously), then the strength of that
connection (weight) is selectively increased.
2. If two neurons on either side of a connection are activated
asynchronously, then that connection is selectively weakened or
eliminated.
• so that chance coincidences do not build up connection strengths.
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19. Hebbian learning Rule
• Example: Consider a 4-input perceptron that uses the bipolar binary sgn
function
+1 if net>0
sgn(net) =
-1 if net<0
• to compute the output value o. If the weight vector w1 = (1 -1 0 0.5) and
given that the learning constant = 1:
• Use the Hebbian learning rule to train the perceptron using each of the
following input vectors:
• x1 = (1 -2 1.5 0)t
• x2 = (1 -0.5 -2 -1.5)t
• x3 = (0 1 -1 1.5)t
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20. Hebbian learning Rule
• Update weight vector for iteration 1
• Update weight vector for iteration 2
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21. Hebbian learning Rule
• Update weight vector for iteration 3
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22. Hebbian learning Rule
• Example: OR function implementation
• Use bipolar data in the place of binary data
• Initially the weights and bias are set to zero
w1=w2=b=0
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X1 X2 B Y (desired)
1 1 1 1
1 -1 1 1
-1 1 1 1
-1 -1 1 -1

23. Hebbian learning Rule
• Update weight vector for iteration 1
• Update weight vector for iteration 2
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Ok
Ok

24. Hebbian learning Rule
• Update weight vector for iteration 3
• Update weight vector for iteration 4
• Since we are given desired outputs we have to correlate them
to the actual outputs i.e. o4 = +1*ydes=-1
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Wrong