Machine Learning

                                                                               UNIT 3 RNN Short Answer Questions 1.      List the different types of Recurrent Neural Networks. 2.      List the different variants in of RNNs with RNN Nodes. 3.      What is the purpose of RNN? 4.      List the different applications of RNNs. 5.      Define RNN Layer. 6.      Define RNN Node. 7.      List the essential elements of RNN. 8.      How can you handle branches in RNN?

   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-1 Deep Learning: Fundamentals Short Answer Questions ----------------------------------------------------------------------------------------------------------------------------- 1.      Define Artificial Neural Network. 2.      Define Neuron. 3.      List the operations performed by ANN layers. 4.      List the different applications of Deep Learning. 5.      Define Deep Learning. 6.      List the different applications of Artificial Neural Network. 7.      List the Building Block of Neural Networks. 8.      Define Dense layer. 9.      What is loss function. 10.   Identify the different layers in ANN. 11.   Explain Forward Pass. 12.   Explain Backward Pass. 13.   List the different optimizers. 14.   How to overcome vanishing and exploding gradient problems 15.   List the difficulties in convergence. 16.   Define Preprocessing. 17.   Define Momentum. 18.   What is Learning Rate Decay? 19.   What is the purpose of weight initialization? 2