Neural Netwrok

NEURAL
NETWORK
1
Presented by: Rabin BK
BSc.CSIT 4th Semester

History
 Introduction
Neural Network in Brief
Application areas
 References
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In 1943, Warren S. McCulloch, a neuroscientist, and Walter Pitts, a logician,
developed the first conceptual model of an artificial neural network
 In their paper, they described the concept of a neuron, a single cell, living in
a network of cells that receives inputs, processes those inputs, and generates
an output.
Donald Hebb took the idea further and created a learning hypothesis based
on the mechanism of neural plasticity that became known as Hebbian
learning, often summarized by the phrase: “Cells that fire together, wire
together.”
The first Hebbian network was successfully implemented at MIT in 1954.
History
3

 Neural plasticity or Neuroplasticity
 The ability of the brain to change throughout an individual's life, e.g., brain activity
associated with a given function can be transferred to a different location, the proportion of
grey matter can change, and synapses may strengthen or weaken over time.
 “Cells that fire together, wire together.”
 Our brain cells communicate with one another via synaptic transmission–one brain cell
releases a chemical (neurotransmitter) that the next brain cell absorbs known as “neuronal
firing”
 When brain cells communicate frequently, the connection between them strengthens.
 Messages that travel the same pathway in the brain over & over begin to transmit faster &
faster. With enough repetition, they become automatic.
 That’s why we practice things like hitting a golf ball–with enough practice, we can go on
automatic pilot.
History contd...
4

A technique for building a computer program that learns from data and
based very loosely on how we think the human brain works.
 They are computing systems vaguely inspired by the biological neural
networks that constitute animal brains
An Artificial Neural Network is based on a collection of connected
units or nodes called artificial neurons which loosely model the neurons
in a biological brain
Introduction to Neural Network
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Biological Neuron Artificial Neuron

Neural Network in Brief
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• First, a collection of software
“neurons” are created and
connected together, allowing them
to send messages to each other.
• Next, the network is asked to
solve a problem, which it attempts
to do over and over, each time
strengthening the connections that
lead to success and diminishing
those that lead to failure.
NASA Space Apps Challenge

 Pattern Recognition
 Examples are facial recognition, optical character recognition, etc.
 Time Series Prediction
 Neural networks can be used to make predictions. For e.g., Will the stock rise or fall
tomorrow? Will it rain or be sunny?
 Signal Processing
 Cochlear implants and hearing aids need to filter out unnecessary noise and amplify the
important sounds. Neural networks can be trained to process an audio signal and filter it
appropriately.
 Control
 In self-driving cars Neural networks are often used to manage steering decisions of
physical vehicles (or simulated ones).
Application of Neural Network
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 Soft Sensors
 A soft sensor refers to the process of analyzing a collection of many measurements.
A thermometer can tell you the temperature of the air, but what if you also knew
the humidity, barometric pressure, dewpoint, air quality, air density, etc.?
 Neural networks can be employed to process the input data from many individual
sensors and evaluate them as a whole.
 Anomaly detection
 Because neural networks are so good at recognizing patterns, they can also be
trained to generate an output when something occurs that doesn’t fit the pattern
 Think of a neural network monitoring your daily routine over a long period of
time.
 After learning the patterns of your behavior, it could alert you when something is
amiss.
Application of Neural Network contd...
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• https://natureofcode.com/book/chapter-10-neural-networks/
• https://medium.com/@Jaconda/a-concise-history-of-neural-networks-
2070655d3fec
• https://www.dailyshoring.com/neurons-that-fire-together-wire-
together/
• https://en.wikipedia.org/wiki/Artificial_neural_network#History
• M., Bishop, Christopher (1995). Neural networks for pattern
recognition. Clarendon Press. ISBN 0198538499. OCLC 33101074.
• https://2016.spaceappschallenge.org/challenges/solar-system/near-
earth-objects-machine-learning/projects/deep-asteriod
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References

Neural Netwrok

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