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NEURAL NETWORKS (Introduction (ANNs (An ANN is configured for a specific…

- - - - Present a training sample to the neural network.
      - Compare the networks output to the desired output from that sample. Calculate error in each output neuron.
      - For each neuron, calculate what the output should have been, and a scaling factor, how much lower or higher the output must be adjusted to match the desired output. This is the local error.
      - Adjust the weights of each neuron to lower the local error.
      - Assign "blame" for the local error to neurons at the previous level, giving greater responsibility to neurons connected by strong weights.
- - - - Single-layer neural networks (Perceptrons)
        
        A neural network in which all the inputs connected directly to the outputs is called a single-layer neural network, or a perceptron network.
      - Multi-layer neural networks (Perceptrons)
        
        The neural network which contains input layers, output layers and some hidden layers also is called a multi-layer neural network.
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        Neural networks versus conventional computers
        
        Neural networks process information in a similar way the human brain does
        
        Neural networks learn by example. They cannot be programmed to perform a specific task.
        
        The way problem is to be solved must be known and stated in small unambiguous instructions.
        
        The examples must be selected carefully otherwise useful time is wasted or even worse the network might be functioning incorrectly
        
        computers would be so much more useful if they could do things that we don't exactly know how to do
        
        Unless the specific steps that the computer needs to follow are known the computer cannot solve the problem.
        
        Conventional computers use an algorithmic approach
        
        Neural networks and conventional algorithmic computers are not in competition but complement each other.
        
        There are tasks more suited to an algorithmic approach like arithmetic operations and tasks that are more suited to neural networks.
        
        Neural networks do not perform miracles. But if used sensibly they can produce some amazing results.