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5.  How does Teachable Machine utilise TensorFlow.js?

                Ans.  Teachable Machine uses TensorFlow.js, an open-source JavaScript library, to run Machine Learning models directly in a
                    web browser, eliminating the need for specialised hardware or software installations.

                  6.  What is the purpose of testing the model in Teachable Machine?
                Ans.  Testing the model in real time allows users to check its performance by using examples, such as pointing the webcam
                    at an object or making a gesture, to see if the model recognises them correctly.

                  7.  What is the goal of creating a classification model on Coral Bleaching?
                Ans.  The  purpose  of  developing  a  classification  model  for  early  identification  of  coral  bleaching  to  safeguard  marine
                    ecosystems.

                  8.  What are the two types of image features mentioned in the text?
                Ans.  The two types of image features are low-level features (such as colour, texture, and intensity) and high-level features
                    (such as shapes and objects).

                  9.  Define Convolution.

                Ans.  Convolution is defined as a simple Mathematical operation that multiplies two numeric arrays of the same dimensions
                    but different sizes to produce a third numeric array of the same dimensions.
                 10.  What makes No-Code AI tools a game-changer for businesses?

                Ans.  No-Code AI tools allow businesses to integrate AI solutions without the need for specialised technical teams, reducing
                    the time and cost involved in AI model development.

                 11.  Explain the function of the Pooling Layer in a CNN.
                Ans.  The Pooling Layer reduces the dimensions of the input image while still retaining the important features. This will help
                    in making the input image more resistant to small transformations, distortions and translations. All this is done to
                    reduce the number of parameters and computation in the network thus making it more manageable and improving
                    the efficiency of the whole system.

                 12.  Why is non-linearity important in a CNN, and how does ReLU introduce it?
                Ans.  Non-linearity is important in a CNN because it allows the network to model complex relationships and patterns within
                    the data, which would be impossible with a purely linear system. The Rectified Linear Unit (ReLU) introduces non-
                    linearity by transforming all negative values in the feature map to zero, while leaving positive values unchanged. This
                    non-linearity helps the network to better model complex patterns by allowing it to activate only the important features
                    and ignore less relevant information (like negative values).

                 13.  How are edges and corners different in image analysis?
                Ans.  Edges represent transitions between regions in an image, while corners represent points where multiple edges meet.
                    Corners are more distinctive and accurate for precise localisation, while edges are less distinctive.

                 14.  Why are No-Code AI platforms gaining popularity?

                Ans.  No-Code  AI  platforms  are  gaining  popularity  because  they  make  AI  accessible  to  a  broader  audience,  allowing
                    businesses and individuals without coding expertise to develop and deploy AI models.








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