An Introduction to Artificial Neural Network

1. AN INTRODUCTION TO
ARTIFICIAL NEURAL NETWORKS
PART - I
Dr.S.SASIKALA
Department of ECE
Kumaraguru College of Technology
Coimbatore
Department of
Electronics and Communication Engineering
Since 1986
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2. INTRODUCTION
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3. What is Learning?
Change is The Result of all True Learning
Leo Buscaglia
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4. What is Learning?
• Learning happens when you observe a
phenomena and recognize a pattern.
• You try to understand this pattern by finding
out if there is any relationship between
the entities involved in that phenomena.
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5. What is Learning?
• Take the example of a simple phenomenon
that we observe daily — the occurrence of day
and night – How do you realize?
Is there a pattern? Yes
Day time: A fixed time period, we
are exposed to light and heat of
the sun.
Night time: Another fixed period,
we are deprived of light and heat
from the sun.
This pattern repeats over and
over and over
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6. What is Learning?
• how this pattern occurs?
• There are 2 entities involved in this observation
— Sun and Earth.
• Is there a relationship between the amount of light(and
heat) originating from the sun and the surface of earth
receiving it.
• The pattern suggests that the surface of the earth
receives the light alternatively
— gets it during the daytime
— does not get it during night-time.
• How is this possible?
— There are many possibilities
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7. What is Learning?
• There are 3 conclusions derived called
“models” that explain the observed
phenomena.
• Model 1: Day/Night is a function of Magical
ON/OFF switch of sun
• Model 2: Day/Night is a function of the
Revolution of Sun around the earth
• Model 3: Day/Night is a function of Rotation of
Earth on its axis
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8. What is Learning?
• The question now arises
— Which model(or function) is more accurate?
As per the observations/findings of different
philosophers/scientists across the ages, Model
3 is the most accurate model which explains
the phenomena of Day and Night.
— We can say, that this model “fits” best for
the observations around this phenomena.
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9. What is Learning?
• Once a model has been built, it can be used
to predict future outcomes for that
phenomena.
• In our example, our model can safely predict
that occurrence of day/night will continue to
happen until, for some reason, the earth stops
rotating or sun runs out of its energy
➢ Will the earth stop rotating?
➢ When will the sun spent all of its energy ?
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10. This is How Humans Learn
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11. Human Learning
• Observing something, identifying a pattern,
building a theory (model) to explain this
pattern and testing this theory to check
whether it fits in most or all observations.
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12. How Human Learn?
Parents Parents
Siblings
Teachers
Parents
Siblings
Teachers
Friends
Parents
Siblings
Teachers
Friends
Society
Experience
Parents
Siblings
Wife
Friends
Society
Colleagues
Parents
Siblings
Wife
Children
Friends
Society
Colleagues
Parents
Siblings
Wife
Children
Grand
Children
Friends
Society
Colleagues
BOOKS BOOKS
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13. Is it possible for a machine to mimic
the process of human learning?
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14. Human vs Machine
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15. Machine Can Mimic
Human Learning Process
• The basic idea remains the same
• As with humans, machines are fed with
observations (data)
• The learning algorithm try to find out a
pattern among the data which best fits the
observations
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16. Human learning vs Machine Learning
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17. Machine Learning
A very powerful extension of
Human Brainpower
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18. Task of Machine Learning
• Pattern Recognition
• Decision Making
• Optimization
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19. Pattern Recognition
A pattern
• is an object, process or event that can be
given a name
• can either be seen physically or it can be
observed
• Eg. Eye colour, finger prints, handwriting
Recognition
• process of identifying the patterns
Pattern recognition
• is identifying patterns in data
• Process of converting the raw data into a
form that is amenable for a machine to use
• Pattern recognition involves classification
and cluster of patterns.
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20. Facial Expression Recognition
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22. Pattern Recognition
• Humans
Can perceive pattern naturally
But more computational time is required
• Machines
Computational speed is very high compared to humans.
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23. Human - Very Good in PR
Humans have
Ability to learn from
experience
Brain with lot of information
processing cells
About 1011 neurons
interconnected to form a vast
and complex network like
structure
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