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This testing data is given as an input to the newly created AI model and the output received is checked and
              evaluated on the basis of:
                 • Accuracy
                 • Precision
                 • Recall

                 • F1 score


                       Neural Networks


              Neural Networks form a base of Deep learning, a subfield of Machine learning where algorithms are inspired by
              the structure of the human brain. Neural networks take in data, train themselves to recognise the patterns in this
              data and then predict the outputs for a new set of similar data. The most impressive aspect of neural networks is
              that once trained, they learn on their own just like human brains.


              Why do we use Neural Networks?
              Neural Networks are a series of algorithms used to recognise hidden patterns in raw data, cluster and classify
              it, and continuously learn and improve. They are used in a variety of applications in stock markets, sales and
              marketing  trends,  risk  assessment  and  fraud  detection.  The  main  advantage  is  that  the  data  features  can  be
              extracted automatically by the machine without the input from the developer. Neural networks are primarily used
              for solving problems with large datasets, like images.




                                                                          Large Neural Network

                                       Model Performance                  SmallNeural Network
                                                                          Medium Neural Network






                                                                     Traditional Machine Learning
                                                                             Algorithms




                                                                Size of Data
              To summarize the need to use neural networks:
                 • It can extract data features automatically without the input from the developer.

                 • It is fast and efficient way to solve problems with large datasets, such as images.
                 • It is essentially a system of machine learning algorithms to perform certain tasks.
                 • 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.


              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.


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