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Step 2: Data Augmentation
                 Data augmentation is the process of increasing the
                 amount  and  diversity  of  data.  We  do  not  collect
                 new  data,  rather  we  transform  the  already  present
                 data.  Data  augmentation  means  increasing  the
                 amount  of  data  by  adding  copies  of  existing  data
                 with small changes. The image given here does not
                 change, but we get data on the image by changing
                 different parameters like colour, rotation, flipping and
                 brightness. New data is added by slightly changing
                 the existing data.

                 In the above example:
                 ●  We apply flipping and rotation transformation to create variations of the original images.

                 ●   We also simulate occlusions such as objects partially blocking the view to train the model to handle obstructed
                    scenarios.
                 Step 3: Data Generation

                 Data generation refers to generating or recording data using sensors. Recording temperature readings of a building
                 is an example of data generation. Recorded data is stored in a computer in a suitable form.














                 In the above example, of self-driving car. Data acquisition is done for creating fake driving scenarios with different
                 road conditions, traffic patterns, weather, and lighting to cover many possible situations.


                               Task                                                 #Experiential Learning




                    Visualise that you are in a big, mysterious forest and searching for hidden treasure.
                    Write four observations you will be making for finding the treasure and categorise them under the
                    heads viz: data discovery, data augmentation and data generation.

                                       Observations                                     Categories


                     1. …………………………..……..………………………..……....……....……..   …………………………..……..………………………..……....……....……..

                     2. …………………………..……..………………………..……....……....……..   …………………………..……..………………………..……....……....……..

                     3. …………………………..……..………………………..……....……....……..   …………………………..……..………………………..……....……....……..

                     4. …………………………..……..………………………..……....……....……..   …………………………..……..………………………..……....……....……..





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