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3.  Output Layer: The output layer is the final layer of the Neural Network. It takes the processed information
                  from the hidden layers and provides the final output, which could be a prediction, classification, or any other
                  desired result based on the problem being solved.

              Real-world  applications  of  Neural  Networks  are  fraud  detection,  recommendation  system,  facial  recognition,
              chatbots and virtual assistant, vegetable price prediction, etc.

                       How does AI make a Decision?


              A perceptron is a basic unit of an Artificial Neural Network that mimics a biological neuron. It takes multiple
              inputs, applies weights, sums them, and passes the result through an activation function to produce an output.
              It serves as a fundamental building block of neural networks, enabling AI to classify and distinguish between
              different inputs based on learned patterns.
              For example, let's say you want to go for a picnic to the park today. What would be your thought process? What
              would you consider?
              On a sunny day, many of us would love to play and enjoy good food in the park. These factors can influence your
              decision about whether to go for a picnic or not. For example, you might ask yourself:

                 • “Should I bring a cricket kit?”                      •  “Should I bring a Tambola game to play?”
                 • "Will there be a big playground to play Cricket?"    •  “Will there be food outlets nearby?”

              These questions help you plan your picnic and make the most of your day out.



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                                              B  B I N G O
                                                  B I N G O
                                             12  12 8  8 28  8 28 15  28 15 2  15 2
                                               60 67 39 26 9
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                                                24
                                               24  11  11  FLAT FLAT  BINGO 47 63  1
                                                    FLAT
                                             24  1 11  BACKGROUND47 63
                                                   BINGO
                                                   BINGO
                                                  BACKGROUND
                                                   BACKGROUND 47 62
                                               1  48 38 22 5
                                                48 38 22
                                              1  26 54 62 62 13  5 5
                                                 48 38 22
                                              26 54 62
                                              26 54  13 13 77 77
                                              www.bingobackground.com  77
                                              www.bingobackground.com
                                             www.bingobackground.com
                   Should I bring a cricket kit?  Should I bring a   Will there be a big playground   Will there be food outlets nearby?
                                            Tambola game to play?      to play Cricket?
              Now, you have the factors that will guide your decision on what to do during the picnic. But take note, not all
              factors are equal. Some factors are more important, while some are not. Let's see which one is more important.
              Let us rank them from the most important to the least important. For me, “Will there be big playground to play
              cricket?” is more important than “Will there be food outlets nearby?”. And “Should I bring a cricket kit?” is more
              important than “Should I bring a Tambola game to play?”. We can put the ranking for this example.
              Now let us convert this to perceptron.
              In the above example, we have four factors as four inputs. So, let’s draw the perceptron with four inputs (from X1
              to X4). Next, we have their weights (from W1 to W4). Then, we also have the bias B, with weight W . Then, we sum
                                                                                                        B
              all up as weighted sum (Σ). Then, the weighted sum is passed through an activation function (∫). The activation
              function is a rule that helps a neuron decide whether to pass its signal forward, allowing the model to handle
              complex problems. Finaly, the output is generated.







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