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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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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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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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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.
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