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.  

UNIT II CNN Long Answer Questions ------------------------------------------------------------------------------------------------------ 1.      Explain CNN with an example. 2.      List the different applications of CNN. 3.      Write an example function for Convolution and Pooling operations and explain in detail. 4.      Draw and explain the architecture of convolution neural networks. 5.      Explain about the convolutional layers in CNN. 6.      Explain striding and padding in CNN with example. 7.      Draw the structure of CNN. 8.      Apply CNN architecture to Classify MNIST Hand Written Dataset. 9.      List the difference between CNN and Fully Connected Layers.

   Deep Learning UNIT   I Deep Learning:   Fundamentals Introduction Building Block of Neural Networks Layers MLPs Forward   pass backward   pass class trainer   and   optimizer The   Vanishing   and   Exploding   Gradient   Problems Difficulties in Convergence Local and Spurious Optima Preprocessing Momentum learning rate Decay Weight Initialization Regularization Dropout SoftMax Cross Entropy loss   function Activation   Functions 👉 Deep Learning: UNIT 1 (A) Notes: Deep Learning: Fundamentals Part 1 Notes 👉 Deep Learning: UNIT 1 (A) PPTs: Deep Learning Fundamentals Part 1 PPTs 👉 Deep Learning: Unit 1 (B) Notes: Deep Learning Fundamentals Part 2 Notes 👉 Deep Learning: UNIT 1 (B): Deep Learning: Fundamentals Part2 PPTs 👉 Deep Learning: UNIT 1: Deep Learning Fundamentals -Long Answer Questions 👉 Deep Learning: UNIT 1: Deep Learning Fundamentals - Short Answer Questions