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4.  ……………………….  works on discrete dataset.

            5.  A neuron is made up of a cell body, an axon and ………………………. .

        C.  State whether these statements are true of false.
            1.  Facial recognition uses neural networks.                                                 ……….……

            2.  ANN is not derived from human neural networks.                                           ……….……

            3.  Neural networks are made up of layers of neurons.                                        ……….……
            4.  Hidden is visible to the outside layer.                                                  ……….……

            5.  Neurons transmit electrical signals to other neurons.                                    ……….……

        D.  Match the following:
            1.  Facial Recognition                                  a.  Rule-based model

            2.  Regression                                          b.  Human Brain
            3.  Neural Networks                                     c.  Machine Learning

            4.  Clustering                                          d.  No Processing
            5.  Input Layer                                         e.  Neural Networks


                                          SECTION B (Subjective Type Questions)

        A.  Short answer type questions:
            1.  What are neural networks?
          Ans.  Neural  networks  are  a  series of  algorithms used  to  recognise  hidden  patterns in raw  data,  cluster  and  classify  it,
              continuously learn and improve.
            2.  Why do we use neural networks?

          Ans.  Neural networks are primarily used for solving problems with large datasets, like images.
            3.  Write the names of two types of neural networks.
          Ans.  Artificial Neural Network and Biological Neural Network.

        B.  Long answer type questions:
            1.  What is a learning-based AI Model? Explain with an example.
          Ans.  Learning-based AI model refers to the AI modelling where the relationship or patterns in data are not defined by the
              developer. In this approach, random data is fed to the machine and it is left on the machine to figure out patterns
              and trends out of it. Generally, this approach is followed when the data is unlabelled and too random for a human to
              make sense out of it. Thus, the machine looks at the  data, tries to extract similar features out of it and clusters the
              same datasets together. In the end as output, the machine tells us about the trends which it observed in the training
              data.
              For example, suppose you have a dataset of 1000 images of random stray dogs of your area. Now you do not have
              any clue as to what trend is being followed in this dataset as you don’t know their breed, or colour or any other
              feature. Thus, you would put this into a learning approach-based AI machine and the machine would come up with
              various patterns that are observed in the features of these 1000 images. It might cluster the data on the basis of
              colour, size, fur style, etc. It might also come up with some very unusual clustering algorithm which you might not
              have even thought of!



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