Machine Learning

  UNIT III RNN ------------------------------------------------------------------------------------------------------------------------ 1.      Handling Branches 2.      Layers 3.      Nodes 4.      Essential Elements 5.      Vanilla RNNs 6.      GRUs 7.      LSTM   👉  Deep Learning: UNIT 3 (A): RNN: Notes 👉 Deep Learning: UNIT 3 (A) : RNN : PPTs 👉  Deep Learning: UNIT- 3: RNN: Long Answer Questions 👉  Deep Learning: UNIT 3 : RNN : Short Answer Questions

                                                                               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 3 RNN Long Answer Questions 1.      Explain RNNLayer with neat diagram. 2.      Explain RNNNode with example. 3.      What is the drawback of Artificial Neural Network? How can you overcome that? Explain. 4.      Explain how RNNs handle sequential data. 5.      Explain the basic three structures of RNN 6.      What is the role of the hidden state in an RNN? 7.      What is a Recurrent Neural Network (RNN) and how does it differ from a feedforward neural network? 8.      Draw and explain the architecture of Recurrent Neural Networks. 9.      Draw and explain Schematic diagram of a recurrent neural network? 10.   List the different types of Recurrent Neural Networks. Explain them with example. 11.   Explain the essential elements of RNNNodes.  

  UNIT III (A) RNN -------------------------------------------------------------------------------------------------------------------- 1.      Handling Branches 2.      Layers 3.      Nodes 4.      Essential Elements

UNIT III (A) RNN 1.      Handling Branches 2.      Layers 3.      Nodes 4.      Essential Elements

   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.

  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