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By: Sameer Ali (09IT43) QUEST, Nawabshah
Artificial Intelligence 09IT
What is a Neural Network?
An Artificial Neural Network (ANN) is an information processing paradigm that is inspired by
the way biological nervous systems, such as the brain, process information. The key element of
this paradigm is the novel structure of the information processing system. It is composed of a
large number of highly interconnected processing elements (neurones) working in unison tosolve specific problems. ANNs, like people, learn by example. An ANN is configured for a
specific application, such as pattern recognition or data classification, through a learning process.
Learning in biological systems involves adjustments to the synaptic connections that exist
between the neurones. This is true of ANNs as well.
Why use neural networks?
Neural networks, with their remarkable ability to derive meaning from complicated or imprecise
data, can be used to extract patterns and detect trends that are too complex to be noticed by either
humans or other computer techniques. A trained neural network can be thought of as an "expert"
in the category of information it has been given to analyse. This expert can then be used toprovide projections given new situations of interest and answer "what if" questions.
Other advantages include:
1. Adaptive learning: An ability to learn how to do tasks based on the data given for trainingor initial experience.
2. Self-Organisation: An ANN can create its own organisation or representation of theinformation it receives during learning time.
3. Real Time Operation: ANN computations may be carried out in parallel, and specialhardware devices are being designed and manufactured which take advantage of this
capability.
4. Fault Tolerance via Redundant Information Coding: Partial destruction of a networkleads to the corresponding degradation of performance. However, some networkcapabilities may be retained even with major network damage.
From Human Neurones to Artificial Neurones
We conduct these neural networks by first trying to deduce the essential features of neurones and
their interconnections. We then typically program a computer to simulate these features.
However because our knowledge of neurones is incomplete and our computing power is limited,
our models are necessarily gross idealisations of real networks of neurones.
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By: Sameer Ali (09IT43) QUEST, Nawabshah
Artificial Intelligence 09IT
The neuron model
A simple neuron
Applications of neural networks
6.1 Neural Networks in Practice
Given this description of neural networks and how they work, what real world applications are
they suited for? Neural networks have broad applicability to real world business problems. In
fact, they have already been successfully applied in many industries.
Since neural networks are best at identifying patterns or trends in data, they are well suited for
prediction or forecasting needs including:
sales forecasting
industrial process control
customer research
data validation
risk management
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By: Sameer Ali (09IT43) QUEST, Nawabshah
Artificial Intelligence 09IT
target marketing
But to give you some more specific examples; ANN are also used in the following specific
paradigms: recognition of speakers in communications; diagnosis of hepatitis; recovery of
telecommunications from faulty software; interpretation of multimeaning Chinese words;
undersea mine detection; texture analysis; three-dimensional object recognition; hand-writtenword recognition; and facial recognition.
Supervised vs. unsupervised learning
From a theoretical point of view, supervised and unsupervised learning differ only in the causal
structure of the model. In supervised learning, the model defines the effect one set of
observations, called inputs, has on another set of observations, called outputs. In other words, the
inputs are assumed to be at the beginning and outputs at the end of the causal chain. The models
can include mediating variables between the inputs and outputs.
In unsupervised learning, all the observations are assumed to be caused by latent variables, thatis, the observations are assumed to be at the end of the causal chain. In practice, models for
supervised learning often leave the probability for inputs undefined. This model is not needed as
long as the inputs are available, but if some of the input values are missing, it is not possible to
infer anything about the outputs. If the inputs are also modelled, then missing inputs cause no
problem since they can be considered latent variables as in unsupervised learning.
Figure 2: The causal structure of (a) supervised and (b)
unsupervised learning. In supervised learning, one set ofobservations, called inputs, is assumed to be the cause of
another set of observations, called outputs, while in
unsupervised learning all observations are assumed to be
caused by a set of latent variables.
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By: Sameer Ali (09IT43) QUEST, Nawabshah
Artificial Intelligence 09IT
Figure2illustrates the difference in the causal structure of supervised and unsupervised learning.
It is also possible to have a mixture of the two, where both input observations and latent
variables are assumed to have caused the output observations.
With unsupervised learning it is possible to learn larger and more complex models than with
supervised learning. This is because in supervised learning one is trying to find the connection
between two sets of observations. The difficulty of the learning task increases exponentially in
the number of steps between the two sets and that is why supervised learning cannot, in practice,
learn models with deep hierarchies.
In unsupervised learning, the learning can proceed hierarchically from the observations into ever
more abstract levels of representation. Each additional hierarchy needs to learn only one step and
therefore the learning time increases (approximately) linearly in the number of levels in the
model hierarchy.
If the causal relation between the input and output observations is complex -- in a sense there is a
large causal gap -- it is often easier to bridge the gap using unsupervised learning instead of
supervised learning. This is depicted in figure3. Instead of finding the causal pathway from
inputs to outputs, one starts building the model upwards from both sets of observations in the
hope that in higher levels of abstraction the gap is easier to bridge. Notice also that the input and
output observations are in symmetrical positions in the model.
Figure 3: Unsupervised learning can be used for bridging thecausal gap between input and output observations. The latent
variables in the higher levels of abstraction are the causes for
both sets of observations and mediate the dependence
between inputs and outputs.
http://users.ics.aalto.fi/harri/thesis/valpola_thesis/node34.html#fig:supunsupcausahttp://users.ics.aalto.fi/harri/thesis/valpola_thesis/node34.html#fig:supunsupcausahttp://users.ics.aalto.fi/harri/thesis/valpola_thesis/node34.html#fig:supunsupcausahttp://users.ics.aalto.fi/harri/thesis/valpola_thesis/node34.html#fig:supunsupgaphttp://users.ics.aalto.fi/harri/thesis/valpola_thesis/node34.html#fig:supunsupgaphttp://users.ics.aalto.fi/harri/thesis/valpola_thesis/node34.html#fig:supunsupgaphttp://users.ics.aalto.fi/harri/thesis/valpola_thesis/node34.html#fig:supunsupgaphttp://users.ics.aalto.fi/harri/thesis/valpola_thesis/node34.html#fig:supunsupcausa7/29/2019 sameer ali domki article
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By: Sameer Ali (09IT43) QUEST, Nawabshah
Artificial Intelligence 09IT
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By: Sameer Ali (09IT43) QUEST, Nawabshah
Artificial Intelligence 09IT
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