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Dendrites in the human brain receive the impulse and pass on to the cell body of the neuron. From the cell body
            the impulse travels to the axon and passes to another neuron attached through a joint called synapses. This
            process goes on through a complex network of neurons to get the desired stimulus of the impulse.
            Similarly, the artificial neurons(nodes) take input data and perform simple operations on the data. The result of
            these operations is passed to other artificial neurons(nodes) which are arranged in a sequence of layers.


                    Working of Neural Networks

            Neural networks are made up of layers of neurons, just like the human brain that consists of millions of neurons.
            These neurons are the core processing units of the network.
            A neural network is divided into multiple layers and each layer is further divided into several blocks called nodes.
            Each node is responsible to do its task and pass on it to the next layer. First, we have the input layer which
            receives the input in several different formats provided by the programmer and feeds it to the neural network.
            No processing occurs in the input layer. The output layer predicts our final output. The output at each node is
            called its activation or node value.
            The layer present in-between input and output layers is called the hidden layers which perform most of the
            computations required by our network. These layers are not visible to the user. Each node of the hidden layer
            has its own machine learning algorithm which it executes on the data received by the input layer. The processed
            data  is  then  fed  to  the  subsequent  hidden  layer.  There  can  be  multiple  hidden  layers  depending  upon  the
            complexity of the function to be performed by the model. The processed data by the hidden layers is passed
            onto the output layer which then gives the final output to the user. No processing is done in the output layer.


                                        Machine Learning                 Machine Learning
                        Data                                                                      Answer
                                     Algorithm + Hidden Rules         Algorithm + Hidden Rules



                                        Machine Learning                 Machine Learning
                        Data                                                                      Answer
                                     Algorithm + Hidden Rules         Algorithm + Hidden Rules



                                        Machine Learning                 Machine Learning
                        Data                                                                      Answer
                                     Algorithm + Hidden Rules         Algorithm + Hidden Rules




                    Types of Neural Networks
            There are mainly two types of Neural Networks:
               • Artificial Neural Network

               • Biological Neural Network
            Now let us learn about them in detail.


            Artificial Neural Networks (ANN)
            It is artificially created efficient computing systems designed to simulate the human brain. It includes machine
            learning as a part of artificial intelligence. An ANN in its training phase is capable of learning by recognising
            patterns in data which is later used to generate the desired output.
            ANN is made up of three basic layers—Input, Hidden and Output. The input layer accepts the inputs, the hidden
            layer processes the inputs, and the output layer produces the result where each layer tries to learn from the
            computed weights. It is the foundation of AI and is used to solve complex problems that are difficult for humans.
            It consists of hardware or software that operates just like neurons of the human brain. Commercial application
            of ANN is in solving complex signal processing, predictions or pattern recognition problems.

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