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function controls the scale of the output. For example, an acceptable range of output is usually
            between 0 and 1, or it could be −1 and 1.

            How do Neural Networks Work?

            The human brain is the inspiration behind artificial neural network architecture. Human brain cells,
            called neurons, form a complex, highly interconnected network and send electrical signals to each
            other to help humans process information. Similarly, an artificial neural network is made of artificial
            neurons that work together to solve a problem. Artificial neurons are software modules called nodes,
            and artificial neural networks are software programs or algorithms that use computing systems to
            solve mathematical calculations.
            ANNs are composed of multiple nodes which imitate biological neurons of human brain. The nodes
            can take input data and perform simple operations on the data. The result of these operations is
            passed to other neurons. The output at each node is called its activation or node value. Each link is
            associated with weight. ANNs are capable of learning, which takes place by altering weight values.

            Simple Artificial Neural Network Architecture
            A basic neural network has interconnected artificial neurons in three layers:
              1.  Input  Layer:  Information  from  the  outside  world  enters  the
                  artificial neural network from the input layer. Input nodes process
                  the data, analyse or categorise it, and pass it on to the next layer.
              2.   Hidden Layer: Hidden layers take their input from the input layer
                  or  other  hidden  layers.  Each  hidden  layer  analyses  the  output
                  from the previous layer, processes it further, and passes it on to
                  the next layer.
              3.  Output Layer: The output layer gives the final result of all the data processing by the artificial
                  neural network. It can have single or multiple nodes.

            Types of Artificial Neural Networks
            Based on the way they operate, Artificial Neural Networks can
            be of several types:

              1. Feed-Forward Neural Network

            It conveys information in one direction through input nodes. The
            information continues to be processed in this single direction
            until it reaches the output mode. This type of ANN is most often
            used for facial recognition technologies.


                                                         2. Recurrent Neural Network
                                                         The  Recurrent  neural  network  takes  the  output  of  a
                                                         processing  node  and  transmits  the  information  back
                                                         into the network.  This  results  in  theoretical  learning
                                                         and  improvement  of  the  network.  This  technique  is
                                                         especially critical for networks in which the prediction
                                                         is  incorrect;  the  system  will  attempt  to  learn  why  the


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