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What did you observe?

              The result will be the opposite of the positive values. The image may
              darken or show an inverse effect. Negative values tend to blur or
              edge-detect the image, creating darker or more shadowed areas.

                 • Create  a  convolution  matrix  that  includes  both  positive  and
                negative values, for example:

              What did you observe?
              This creates a mix of effects on the image. The positive and negative values in the kernel work together to
              produce a more complex transformation. You might notice sharpening, edge detection, or contrast changes in
              the image. The result depends on the weight of the positive and negative values and how they interact with the
              pixel values in the image.


                            Task                                                         21 st  Century   #Technology Literacy
                                                                                             Skills


                Visit the https://setosa.io/ev/image-kernels/ link and change the value in the given image kernel.

                Now answer the following questions:
                1.  Make 4 numbers negative. Keep the rest as it is. Write your observation:
                2.  Now, change the center value to negative. Write your observation:
                3.   Now, change the second value of each row to positive. Keep the rest
                   as it is. Write your observation:
                4.  Now, what effect did you apply to final image?



                       What is Neural Network?


              Neural Networks are a series of algorithms used to recognise hidden patterns in raw data, process it, cluster
              and classify it, and continuously learn and improve. They are used in a variety of applications such as predicting
              stock prices, identifying sales and marketing trends, risk assessment, and fraud detection. The main advantage of
              neural network 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.
              A Neural Network is divided into multiple layers and each layer is further divided into several units or neurons,
              also known as nodes Each neuron processes its inputs, applies a mathematical function and passes the result to
              the next layer. First, we have the input layer which receives the input in several different formats provided by the
              programmer and feeds it to the neural network. Minimal processing occurs in the input layer, as it simply passes
              the raw input data forward. The output layer produces the final prediction or decision based on the learned
              patterns. The output at each node is called its activation or node value.

                       What is Convolutional Neural Network (CNN)?


              Convolutional Neural Network is a type of Artificial Neural Network and is made up of neurons that help in image
              recognition and image processing. It uses a deep learning algorithm that takes an input image, processes it by assigning
              learnable weights and biases to various aspects/objects in the image, enabling the network to identify patterns and
              features helping the system differentiate one image from the other with maximum accuracy. CNNs reduce the spatial
              dimensions (size) of the input through operations like pooling, while retaining the essential features and give the predicted
              class probabilities for the input image. They are trained to identify and extract the best features from the images.

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