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

 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   UNIT II CNN: Introduction, striding and padding, pooling layers , structure, o

Deep Learning   👉 Deep Learning Syllabus 👉 Deep Learning: Fundamentals 👉 CNN 👉 RNN 👉Autoencoders 👉Transfer   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. UNIT II CNN: Introduction, striding and padding, pooling layers, structure, operations and prediction of CNN with layers, CNN -Case study with MNIST, CNN VS Fully Connected UNIT III RNN: Handling Branches, Layers, Nodes, Essential Elements-Vanilla RNNs, GRUs, LSTM UNIT IV Autoencoders: Denoising Autoencoders, Sparse Autoencoders, Deep Autoencoders, Variational Autoencoders, GANS UNIT V Transfer Learning - Types, Methodologies, Diving into Transfer Learn