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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              Answer
                         Data
                                         Algorithm + Hidden Rules        Algorithm + Hidden Rules





                             Video Session

                       Watch this video,
                       Visit https://www.youtube.com/watch?v=bfmFfD2RIcg or scan the QR code and answer
                       the following question.
                       What did you learn from this video?





                 Important Features of Neural Networks
                 Some of the important features of Neural Network are:

                    • The model of the AI Neural network is based on the human neural network i.e., brain and Nervous system.
                    • They are designed in such a way that the information can be automatically extracted without the interaction of
                   the programmer.
                    • Every node of a neural network system is a machine Learning algorithm.
                    • It is best suited for AI models dealing with large data.







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