The document discusses various types of neural networks including feedforward neural networks, recurrent neural networks, and competitive networks. It describes the basic components and architecture of neural networks including input, hidden, and output layers. It also covers different learning paradigms such as supervised learning, unsupervised learning, and reinforcement learning. Supervised learning involves training a network using labeled input-output pairs, while unsupervised learning uses unlabeled data and allows the network to find hidden patterns in the data. Reinforcement learning involves learning through trial-and-error interactions with an environment.

1. Dr. Meenakshi Sood
Associate Professor, NITTTR Chandigarh
meenkashi@nitttrchd.ac.in
Input
signal
Synaptic
weights
Summing
function
Bias
b
Activation
function
Local
Field
v Output
y
x1
x2
xm
w2
wm
w
1
 
 )
(


3. 3
Properties of Artificial Neural Nets
(ANNs)
 Many simple neuron-like threshold switching units
 Many weighted interconnections among units
 Input is high-dimensional discrete or real-valued (e.g.
raw sensor input)
 Output is discrete or real valued
 Output is a vector of values
 Form of target function sometimes unknown
 Highly parallel, distributed processing
 Learning by tuning the connection weights
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4. 4
Types of Layers
• The input layer.
– Introduces input values into the network.
– No activation function or other processing.
• The hidden layer(s).
– Perform classification of features
– Two hidden layers are sufficient to solve any problem
– Features imply more layers may be better
• The output layer.
– Functionally just like the hidden layers
– Outputs are passed on to the world outside
the neural network.
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5. Neural Network Topologies
Single layer feed-forward networks
 Input layer projecting into the output layer
Input Output
layer layer
Single layer
network
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6. Cont...
Multi-layer feed-forward networks
 One or more hidden layers. Input projects only from
previous layers onto a layer.
Input Hidden Output
layer layer layer
2-layer or
1-hidden layer
fully connected
network
Organized into different layers
Unidirectional connections
memory-less: output depends only on the present
input
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7. k
O
jk
w
Output nodes
Input nodes
Hidden nodes
Output Class
Input Record : xi
wij - weights
Network is fully connected
j
O
A Multilayer Neural Network
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8. Neural Netwok Architecture
 Feed forward Architecture
 Feedback (Recurrent ) Architecture
 Competitive network
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9. Feed Forward Neural Networks
 The information is propagated
from the inputs to the outputs
 The first layer is the input and the
last layer is the output.
 The activities of the neurons in
each layer are a non-linear
function of the activities in the
layer below.
 Time has no role (NO cycle
between outputs and inputs)
x1 x2 xn
…..
1st hidden
layer
2nd hidden
layer
Output layer
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10. Recurrent Neural Networks
 A network with feedback, where some of its inputs are
connected to some of its outputs (discrete time).
 Delays are associated to a specific weight
 Training is more difficult
 Stable Outputs may be more difficult to evaluate
 Unexpected behavior (oscillation, chaos, …)
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11. 11/48
Architectures:
Feedforward and Feedback
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12. 12/48
Feedforward vs Feedback:
 Possess no dynamics
 Demonstrate powerful properties
 Universal function approximation
 Find widespread applications in pattern
classification.
X ∈ Rn S = f (X)
Multilayer Perceptrons
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13. 13/48
Feedforward vs
Feedback:
 Non-linear dynamical systems
 New state of the network is a
function of the current input and
the present state of the network
 Capable of performing powerful
tasks such as
 pattern completion
 topological feature
mapping
 pattern recognition
Recurrent Neural Networks
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14. Competitive Networks ( later
sessions)
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15. Neural Network types can be classified based on following attributes:
• Applications
-Classification
-Clustering
-Function approximation
-Prediction
• Connection Type
- Static (feedforward)
- Dynamic (feedback)
• Topology
- Single layer
- Multilayer
- Recurrent
- Self-organized
• Learning Methods
- Supervised
- Unsupervised
SUMMARY
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16. TRAINING A NEURAL NETWORK
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17. Overview
The ability of the neural network (NN) to learn from
its environment and to improve its performance
through learning.
- The NN is stimulated by an environment
- The NN undergoes changes in its free parameteres
- The NN responds in a new way to the environment
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18. Definition of Learning
 Learning is a process by which the free parameters of a
neural network are adapted through a process of
stimulation by the environment in which the network
is embedded.
 The type of the learning is determined by the manner
in which the parameter changes take place.
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19. A Simple Artificial Neuron
It receives input from some other units, or perhaps from an external source.
Each input has an associated weight w, which can be modified so as to model
synaptic learning.
The unit computes some function f of the weighted sum of its inputs
•Its output, in turn, can serve as input to other units.
•Note that wij refers to the weight from unit j to unit
i (not the other way around).
•The function f is the unit's activation function. In the
simplest case, f is the identity function, and the unit's
output is just its net input. This is called a linear unit.
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20. x y
Hidden Neurons
Output Neuron
Input
Output
yhid,1
(u)
yout
(u)
yhid,2
(u)
yhid,N
(u)
whid,1
whid,2
wout,1
wout,2
wout,N
whid,N
Adjust weights (w) to learn a given target
function: y = f(x)
Given a set of training data X→Y
Represent a single input single output functions: y = f(x)
INPUT DATA SET- X
CORRESPONDING OUTPUT - Y
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21. x1 y1
Hidden Neurons Output Neurons
Inputs Outputs
x2 y2
xn ym
yh1
(u) yo1
(u)
yo2
(u)
yh2
(u)
yhn
(u) yom
(u)
 Weight update formula:
w
k
w
k
w 


 )
(
)
( 1
w
i
e
i
w




)
(
*
)
( 
2
2
1
)
( train
out y
y
e 

ERROR FUNCTION/ COST FUNCTION
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22. LEARNING PROCESS
 NN is stimulated by environment
 NN undergoes changes in its free parameter( weights)
 NN responds in a new way to the environment
Set of well defined rules fort the solution is called LEARNING ALGORITHM
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23. 23
Learning Rule
 Learning rule: a procedure (training algorithm) for
modifying the weights and the biases of a network.
 The purpose of the learning rule is to train the
network to perform some task.
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24. Supervised learning,
Unsupervised learning and
Reinforcement learning.
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25. Rudimentary Example of classification
for Understanding Supervised and Unsupervised
Learning
A
B
A
B A
B
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26. Two possible Solutions…
A
B
A
B
A
B
A
B A
B
A
B
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27. Supervised Learning
 It is based on a labeled
training set.
 The class of each piece
of data in training set is
known.
 Class labels are pre-
determined and
provided in the training
phase.
A
B
A
B
A
B
 Class
 Class
 Class
 Class
 Class
 Class
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28. 28
Supervised Learning
 The learning rule is provided with a set of examples (the
training set) of proper network behavior: {x1,y1},
{x2,y2},…, {xQ,yQ} where xq is an input to the network
and yq is the corresponding correct (target) output.
 As the inputs are applied to the network, the network
outputs are compared to the targets. The learning rule is
then used to adjust the weights and biases of the network
in order to move the network outputs closer to the targets.
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29. Supervised learning
 In supervised training, both the inputs and the outputs are
provided.
 The network then processes the inputs and compares its resulting
outputs against the desired outputs.
 Errors are then propagated back through the system, causing the
system to adjust the weights which control the network.
 This process occurs over and over as the weights are continually
tweaked. The set of data which enables the training is called the
training set.
 During the training of a network the same set of data is processed
many times as the connection weights are ever refined.
 Example architectures : Multilayer perceptions
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30. Learning Paradigms
 Learning with a Teacher (=supervised learning)
 The teacher has knowledge of the environment
 Error-performance surface
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31. Unsupervised learning
 In unsupervised training, the network is provided with
inputs but not with desired outputs.
 The system itself must then decide what features it will use
to group the input data. This is often referred to as self-
organization or adaption.
 Example architectures : Kohonen, ART
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32. 4/8/2020 32
Unsupervised Learning
 The weights and biases are modified in response
to network inputs only. There are no target
outputs available.
 Most of these algorithms perform some kind of
clustering operation. They learn to categorize
the input patterns into a finite number of classes.
This is especially in such applications as vector
quantization.
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33. Unsupervised Learning
 There is no external teacher or critic to oversee the
learning process.
 The provision is made for a task independent measure
of the quality of representation that the network is
required to learn.
The training process extracts the statistical properties of the
training set and groups similar vectors into classes.
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34. Reinforcement learning
 The learning of input-output mapping is
performed through continued interaction with
the environment in oder to minimize a scalar
index of performance.
Task independent measure
Delayed Reinforcement
Credit assignment
Minimize a cost function
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35. 4/8/2020 35
Reinforcement Learning
 The learning rule is similar to supervised
learning, except that, instead of being provided
with the correct output for each network input,
the algorithm is only given a grade.
 The grade (score) is a measure of the network
performance over some sequence of inputs.
 It appears to be most suited to control system
applications.
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36. Supervised Vs Unsupervised
 Task performed
Classification
Pattern
Recognition
 NN model :
Preceptron
Feed-forward NN
 Task performed
Clustering
 NN Model :
Self Organizing
Maps
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37. 37
Learning algorithm
Epoch : Presentation of the entire training set to the neural network.
In the case of the AND function an epoch consists of four sets of
inputs being presented to the network (i.e. [0,0], [0,1], [1,0], [1,1])
Error: The error value is the amount by which the value output by the network
differs from the target value. For example, if we required the network
to output 0 and it output a 1, then Error = -1
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38. 38
Learning algorithm
Target Value, T : When we are training a network we not only present it with
the input but also with a value that we require the network to
produce. For example, if we present the network with [1,1] for the
AND function the training value will be 1
Output , O : The output value from the neuron
Ij : Inputs being presented to the neuron
Wj : Weight from input neuron (Ij) to the output neuron
LR : The learning rate. This dictates how quickly the network converges. It is set
by a matter of experimentation. It is typically 0.1
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