Neural Network and Artificial Intelligence. Neural Network and Artificial Intelligence. WHAT IS NEURAL NETWORK? The method calculation is based on the interaction of plurality of processing elements inspired by biological nervous system called neurons. It is a powerful technique to solve real world problem. A neural network is composed of a number of nodes, or units[1], connected by links. Each linkhas a numeric weight[2]associated with it. . Weights are the primary means of long-term storage in neural networks, and learning usually takes place by updating the weights. Artificial neurons are the constitutive units in an artificial neural network. WHY USE NEURAL NETWORKS? It has ability to Learn from experience. It can deal with incomplete information. It can produce result on the basis of input, has not been taught to deal with. It is used to extract useful pattern from given data i.e. pattern Recognition etc. Biological Neurons Four parts of a typical nerve cell :• DENDRITES: Accepts the inputs• SOMA : Process the inputs• AXON : Turns the processed inputs into outputs.• SYNAPSES : The electrochemical contactbetween the neurons.  ARTIFICIAL NEURONS MODEL Inputs to the network arerepresented by the x1mathematical symbol, xn Each of these inputs are multiplied by a connection weight , wn sum = w1 x1 + ……+ wnxn These products are simplysummed, fed through the transfer function, f( ) to generate a result and then output.  NEURON MODEL Neuron Consist of: Inputs (Synapses): inputsignal.Weights (Dendrites):determines the importance ofincoming value.Output (Axon): output toother neuron or of NN . 

1. NEURAL NETWORK
And Artificial Intelligence
Presented by:
Saddam Hussain, Owais Latif and Qazi

2. WHAT IS NEURAL NETWORK?
• The method calculation is based on the
interaction of plurality of processing
elements inspired by biological nervous
system called neurons.
• It is a powerful technique to solve real
world problem.
• A neural network is composed of a
number of nodes, or units[1], connected
by links. Each link
has a numeric weight[2]associated with it.
.
1. Weights are the primary means of long-term storage in
neural networks, and learning usually takes place by
updating the weights.
2. Artificial neurons are the constitutive units in an artificial
neural network

3. WHY USE NEURAL NETWORKS?
• It has ability to Learn from experience.
• It can deal with incomplete information.
• It can produce result on the basis of input, has not
been taught to deal with.
• It is used to extract useful pattern from given data i.e.
pattern Recognition etc.

4. Biological Neurons
Four parts of a typical nerve cell :
• DENDRITES: Accepts the inputs
• SOMA : Process the inputs
• AXON : Turns the processed
inputs into outputs.
• SYNAPSES : The
electrochemical contact
between the neurons.

5. ARTIFICIAL NEURONS MODEL
• Inputs to the network are
represented by the x1
mathematical symbol, xn
• Each of these inputs are multiplied by
a connection weight , wn
sum = w1 x1 + ……+ wnxn
• These products are simply
summed, fed through the transfer
function, f( ) to generate a result and
then output.

6. NEURON MODEL
Neuron Consist of:
Inputs (Synapses): input
signal.
Weights (Dendrites):
determines the importance of
incoming value.
Output (Axon): output to
other neuron or of NN .

7. Terminology
Biological Terminology Artificial Neural Network Terminology
Neuron Node/Unit/Cell/Neurode
Synapse Connection/Edge/Link
Synaptic Efficiency Connection Strength/Weight
Firing frequency Node output

8. The Learning Process
• Associative mapping in which the network learns to
produce a particular pattern on the set of input units
whenever another particular pattern is applied on the set
of input units.
 Auto-Association.
 Hetero-Association.

9. Associate Mapping
Auto-Association.
An input pattern associated with itself , and the states of
input and output units coincides, this used to provide
pattern completion.
Hetero-association.
Nearest-neighbour recall, where the output pattern
produced corresponds to the input pattern stored, which is
closest to the pattern presented, and
Interpolative recall, where the output pattern is a
similarity dependent interpolation of the patterns stored
corresponding to the pattern presented. Yet another
paradigm, which is a variant associative mapping is
classification, i.e. when there is a fixed set of categories
into which the input patterns are to be classified.

10. • This is learning by doing.
• In this approach no sample
outputs are provided to the
network against which it can
measure its predictive
performance for a given vector of
inputs.
• One common form of
unsupervised learning is
clustering where we try to
categorize data in different
clusters by their similarity.
UNSUPERVISED LEARNING
• A teacher is available to indicate
whether a system is performing
correctly, or to indicate the amount of
error in system performance. Here a
teacher is a set of training data.
• The training data consist of pairs of
input and desired output values that
are traditionally represented in data
vectors.
• Supervised learning can also be
referred as classification, where we
have a wide range of classifiers,
(Multilayer perceptron , k nearest
neighbor..etc)
SUPERVISED LEARNING

11. THE BACKPROPAGATION ALGORITHM
• The backpropagation algorithm (Rumelhart and
McClelland, 1986) is used in layered feed-forward Artificial
Neural Networks.
• Back propagation is a multi-layer feed forward, supervised
learning network based on gradient descent learning rule.
• we provide the algorithm with examples of the inputs and
outputs we want the network to compute, and then the
error (difference between actual and expected results) is
calculated.
• The idea of the backpropagation algorithm is to reduce
this error, until the Artificial Neural Network learns the
training data.

12. Conventional Computer V/S
Neural Networks
Conventional
computers use an
algorithmic approach,
but neural networks
works similar to human
brain and learns by
example.

13. ADVANTAGES OF ANN:
• It involves human like thinking.
• They can handle noisy or missing data.
• They can work with numbers of variables or parameters.
• They provide general solution with predictive solution.
• System has got property of continuous learning.

14. IMPORTANT APPLICATIONS OF NEURAL
NETWORKS
• Finger Print Recognition System.
• Pre-processing System.
• Feature Extraction Using Neural Networks
• Classifications
• Results

15. Some Other Applications
• Character Recognition - The idea of character recognition
has become very important as handheld devices like the
Palm Pilot are becoming increasingly popular. Neural
networks can be used to recognize handwritten
characters.
• Image Compression - Neural networks can receive and
process vast amounts of information at once, making
them useful in image compression. With the Internet
explosion and more sites using more images on their
sites, using neural networks for image compression is
worth a look.

16. Cont.
• Stock Market Prediction - The day-to-day business of the
stock market is extremely complicated. Many factors weigh
in whether a given stock will go up or down on any given
day. Since neural networks can examine a lot of information
quickly and sort it all out, they can be used to predict stock
prices.
• Travelling Salesman Problem- Interestingly enough, neural
networks can solve the travelling salesman problem, but
only to a certain degree of approximation.
• Medicine, Electronic Nose, Security, and Loan
Applications - These are some applications that are in their
proof-of-concept stage, with the acceptance of a neural
network that will decide whether or not to grant a loan,
something that has already been used more successfully
than many humans.

17. THANK YOU