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Conventional Computer |
Neural Network |
Inputs |
- A few inputs like a keyboard, mouse or bar code scanner.
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- Many inputs, possibly thousands.
- For example an image recognition system would have inputs for each pixel.
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Processor |
- A single processor which deals with all the data.
- Some computers have two or four processors but rarely more.
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- Many processors, possibly thousands.
- Each processor is much simpler than a conventional computer processor.
- Each processor deals with a small part of the data.
- The processors work in parallel.
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Programming |
- Must be programmed by a programmer.
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- Must be trained.
- Neural networks learn instead of being programmed.
- Learning takes place by links being made stronger or weaker.
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Memory |
- Data stored centrally (ROM, RAM, hard disk)
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- Data stored as weightings in the network.
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Applications |
Solves well defined problems like
- payroll or
- bank account management.
- Can do precise maths.
- Good for solving problems with small numbers of inputs and where the algorithm (set of rules) for solving the problem is well understood.
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Better at predictive problems such as ...
- Weather forecasting
- Image recognition. Solves problems like image recognition (spot the criminal or terrorist in the shopping mall).
- Good for solving problems with vast amounts of input data where the rules for reaching the answer are fuzzy or not well defined.
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