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• The larger neural networks tend to perform better with larger amounts of data whereas the traditional machine
              learning algorithm stops improving after a certain saturation point. See the figure:


                    Applications of Neural Networks

            Some of the applications of neural networks are:
               • Facial recognition: Cameras on smartphones these days can estimate
              the age of the person based on their facial features. This is neural
              networks at play. First differentiating the face from the background
              and then correlating the lines and spots on your face to a possible age.
              For example, Facebook uses facial recognition powered by artificial
              neural networks to suggest to you whom you should tag in the post.








                                                       • Forecasting: Neural networks are trained to understand the patterns
                                                      and detect the possibility of rainfall or rise in stock prices with high
                                                      accuracy.



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                                                                                          3       2

               • Music Composition: Neural networks can even learn patterns
              in music and train themselves enough to compose fresh tunes.                        Eb6        Dc7
                                                                                                  6




                    Advantages of Neural Network

            Some of the advantages of neural networks are as follows:
               • Parallel processing capability:  Artificial neural networks is a very powerful system that can perform more than
              one job at the same time.
               • Data is stored on the entire network: Since the data is available on the entire network so if any node is down
              or unavailable, the whole system will not stop working.
               • Capable of learning from non-linear and complex data: The input can be complex and non-linear for ANN
              to use it to generate the desired output.


                    AI Models

            There are mainly three types of AI models: Regression, Classification
            and Clustering. Let us learn about them in detail.

            Regression

            Regression is an example of rule-based AI models. This is a type of Rule-
            Based AI model. In regression, the  algorithm  generates a  mapping

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