Machine Learning

   UNIT II CNN Short Answer Questions --------------------------------------------------------------------------------------------------------------------------- 1.      List the applications of CNN. 2.      Define Convolution. 3.      Define Stride. 4.      Explain padding. 5.      What the purpose of padding? 6.      Define Kernal. 7.      Define Pooling. 8.      List the different pooling techniques. 9.      Define Flattening. 10.   Define Fully Connected Layer. 11.   List the difference between CNN and Fully Connected Layer. 12.   What is a filter (or kernel) in the context of a CNN? 13.   Discuss the role of fully connected layers in CNNs. 14.   Explain the concept of pooling in CNNs and how it sometimes impacts output size and can cause underfitting. 15.   What is the purpose of the pooling layer in a CNN? 16.   what are two special cases of padding? explain them with a neat diagram. 17.   Discuss the concept of convolution in CNNs. 18.  

  Q1. CNN features Why is convolution neural network taking off quickly in recent times? Choose the correct answer from below: A.      Access to large amount of digitized data B.      Integration of feature extraction within the training process C.       Availability of more computational power D.      All the above Ans: All the above  is the correct answer. Using CNN, we can Access and train our model on a large amount of digitized data Unlike classical image D recognition where you define the image features yourself, CNN takes the image’s raw pixel data, trains the model, then extracts the features automatically for better classification. Using CNN, the number of training parameters is reduced significantly. And due to the availability of more computational power in recent times. The model takes less time to train. Q2. Recognizing a cat For an image recognition problem (recognizing a cat in a photo), which of the f