Presented By: Stuart Monaghan
• Introduction to Neural Networks (NN)
• History of Neural Networks
• Types of Neural Networks
• Who is concerned with Neural Networks?
• Why would anyone want a new sort of Computer?
– What are Computers good at?
• Fast Arithmetic.
• Doing precisely what the programmer programs them to do.
– What are Computers not good at?
• Interacting with data concerned with the environment.
• Able to do large parallel computation.
• Fault tolerance.
• Adapting to circumstance.
• Neural Networks
– Also known as Artificial Neural Networks.
– They consist of many simple processing units
(or neurons) connected together.
– Has the ability to learn from the environment,
and adapt to it in a manner similar to human
• The Analogy to the Brain
– Modeled after the structure of the brain.
• The Biological Neuron
– Most basic element of the human brain =
– Ability to remember, think, and apply previous
experiences to our every action.
The Human Brain & The Neural Network
• During the 40’s enthusiasm was high.
• The Manhattan Project
• Was believed that with enough resources
the problem of intelligence could be solved.
• Disappointment followed.
• “In the 1960 it was predicted that within ten years
computers would convert ordinary speech and handwriting
to print, comprehend and compose natural language, drive
trucks, do housework, and tutor students better than
teachers could. Thirty years later many proponents see no
reason to change these predictions; they still expect them
within ten years”. Thomas K. Landauer
• There are many kinds of Neural Networks!
• Two ways of implementing NN.
– 1 Feedforward
• the connections between the units do not form cycles.
• Response to data input is fast.
– 2 Feedback
• There are cycles in the connections.
• NN must loop for a long time before generating a response.
Feed forward topology
Courtesy of URL:http://cse.stanford.edu/classes/sophomore-college/projects-00/neural-
Who is Concerned?
• Neural Networks involve the following
• Computer Scientists
• Amongst others...
• Start processing data without any preconceived hypothesis.
• Unbiased and better understanding of data.
• Once trained, speed and accuracy increases.
• In return cost is saved.
• Training the network needs experts and is time consuming
• Does not give reasons for decisions.
• Neural Networks can be used for
forecasting applications such as:
• Investment Analysis - stock currencies prediction
• Signature Analysis
• The subject of Neurocomputing is vast and
constantly expanding into something that
can change the way computers operate.
• I think Neural Networks has the potential to
grow into changing our perception of