what is neural network....???
TRANSCRIPT
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Introduction to Neural Networks
Presented by:
Hafiz Syed Adnan Ahmed
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Introduction
• Artificial Neural Network is based on the biological nervous
system as Brain
• It is composed of interconnected computing units called
neurons
• ANN like human, learn by examples
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Why Artificial Neural Networks?There are two basic reasons why we are interested in building artificial neural networks (ANNs):
• Technical viewpoint: Some problems such as character recognition or the prediction of future states of a system require massively parallel and adaptive processing.
• Biological viewpoint: ANNs can be used to replicate and simulate components of the human (or animal) brain, thereby giving us insight into natural information processing. 3
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Science: Model how biological neural systems, like human brain, work?
• How do we see?• How is information stored in/retrieved
from memory?• How do you learn to not to touch fire?• How do your eyes adapt to the
amount of light in the environment?• Related fields: Neuroscience,
Computational Neuroscience, Psychology, Psychophysiology, Cognitive Science, Medicine, Math, Physics. 4
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Real Neural Learning
• Synapses change size and strength with experience.
• Hebbian learning: When two connected neurons are firing at the same time, the strength of the synapse between them increases.
• “Neurons that fire together, wire together.”
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Biological Neurons• Human brain = tens of thousands
of neurons• Each neuron is connected to
thousands other neurons• A neuron is made of:
• The soma: body of the neuron• Dendrites: filaments that provide
input to the neuron• The axon: sends an output signal• Synapses: connection with other
neurons – releases certain quantities of chemicals called neurotransmitters to other neurons
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Modeling of Brain Functions
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Modelling a Neuron
• aj :Activation value of unit j
• wj,I :Weight on the link from unit j to unit i
• inI :Weighted sum of inputs to unit i
• aI :Activation value of unit i• g :Activation function
j
jiji aWin ,
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What is an artificial neuron ?
• Definition : Non linear, parameterized function with restricted output range
1
10
n
iiixwwfy
x1 x2 x3
w0
y
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Simple Neuron
X1
X2
Xn
OutputInputs
b
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An Artificial Neuron
x1
x2
xn
…
Wi,1
Wi,2
…
Wi,n
n
jjjii txtwt
1, )()()(net
xi
neuron i
net input signal
synapses
output ))(()(x tnetft iii
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Activation functions
0 2 4 6 8 10 12 14 16 18 200
2
4
6
8
10
12
14
16
18
20
xy
-10 -8 -6 -4 -2 0 2 4 6 8 10-2
-1.5
-1
-0.5
0
0.5
1
1.5
2
-10 -8 -6 -4 -2 0 2 4 6 8 10-2
-1.5
-1
-0.5
0
0.5
1
1.5
2
Linear
Logistic
Hyperbolic tangent
)exp(1
1
xy
)exp()exp(
)exp()exp(
xx
xxy
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How do NNs and ANNs work?• Information is transmitted as a series of
electric impulses, so-called spikes.
• The frequency and phase of these spikes encodes the information.
• In biological systems, one neuron can be connected to as many as 10,000 other neurons.
• Usually, a neuron receives its information from other neurons in a confined area
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Navigation of a car
• Done by Pomerlau. The network takes inputs from a 34X36 video image and a 7X36 range finder. Output units represent “drive straight”, “turn left” or “turn right”. After training about 40 times on 1200 road images, the car drove around CMU campus at 5 km/h (using a small workstation on the car). This was almost twice the speed of any other non-NN algorithm at the time.
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Automated driving at 70 mph on a public highway
Camera image
30x32 pixelsas inputs
30 outputsfor steering
30x32 weightsinto one out offour hiddenunit
4 hiddenunits
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Computers vs. Neural Networks
“Standard” Computers Neural Networks
one CPU highly parallelprocessing
fast processing units slow processing units
reliable units unreliable units
static infrastructure dynamic infrastructure
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Neural Network
Input Layer Hidden 1 Hidden 2 Output Layer
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Network Layers
The common type of ANN consists of three layers
of neurons: a layer of input neurons connected to
the layer of hidden neuron which is connected to
a layer of output neurons.
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Architecture of ANN
• Feed-Forward networksAllow the signals to travel one way from input to
output• Feed-Back NetworksThe signals travel as loops in the network, the
output is connected to the input of the network
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Comparison of Brains and Traditional Computers
• 200 billion neurons, 32 trillion synapses
• Element size: 10-6 m
• Energy use: 25W• Processing speed: 100 Hz• Parallel, Distributed• Fault Tolerant• Learns: Yes• Intelligent/Conscious:
Usually
• 1 billion bytes RAM but trillions of bytes on disk
• Element size: 10-9 m• Energy watt: 30-90W (CPU)• Processing speed: 109 Hz• Serial, Centralized• Generally not Fault Tolerant• Learns: Some• Intelligent/Conscious:
Generally No
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Neural Networks (Applications)
• Face recognition• Time series prediction• Process identification• Process control• Optical character recognition• Adaptative filtering• Etc…
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And Finally….
“If the brain were so simple that we could understand it then we’d be so simple that
we couldn’t”
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Introduction is End of Neural Networks